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Identification of Hemorrhage and Infarct Lesions on Brain CT Images using Deep Learning (2307.04425v1)
Published 10 Jul 2023 in eess.IV and cs.CV
Abstract: Head Non-contrast computed tomography (NCCT) scan remain the preferred primary imaging modality due to their widespread availability and speed. However, the current standard for manual annotations of abnormal brain tissue on head NCCT scans involves significant disadvantages like lack of cutoff standardization and degeneration identification. The recent advancement of deep learning-based computer-aided diagnostic (CAD) models in the multidisciplinary domain has created vast opportunities in neurological medical imaging. Significant literature has been published earlier in the automated identification of brain tissue on different imaging modalities. However, determining Intracranial hemorrhage (ICH) and infarct can be challenging due to image texture, volume size, and scan quality variability. This retrospective validation study evaluated a DL-based algorithm identifying ICH and infarct from head-NCCT scans. The head-NCCT scans dataset was collected consecutively from multiple diagnostic imaging centers across India. The study exhibits the potential and limitations of such DL-based software for introduction in routine workflow in extensive healthcare facilities.
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IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Utz, S., Humphreys, G.W., Chechlacz, M.: Parietal substrates for dimensional effects in visual search: evidence from lesion-symptom mapping. Brain : a journal of neurology 136 Pt 3, 751–60 (2012) Haji and Naval [2020] Haji, S., Naval, N.S.: Management of intracerebral hemorrhage. Evidence-Based Critical Care (2020) Rymer [2011] Rymer, M.M.: Hemorrhagic stroke: intracerebral hemorrhage. Missouri medicine 108 1, 50–4 (2011) Phipps and Cronin [2020] Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Haji, S., Naval, N.S.: Management of intracerebral hemorrhage. Evidence-Based Critical Care (2020) Rymer [2011] Rymer, M.M.: Hemorrhagic stroke: intracerebral hemorrhage. Missouri medicine 108 1, 50–4 (2011) Phipps and Cronin [2020] Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rymer, M.M.: Hemorrhagic stroke: intracerebral hemorrhage. Missouri medicine 108 1, 50–4 (2011) Phipps and Cronin [2020] Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Haji, S., Naval, N.S.: Management of intracerebral hemorrhage. Evidence-Based Critical Care (2020) Rymer [2011] Rymer, M.M.: Hemorrhagic stroke: intracerebral hemorrhage. Missouri medicine 108 1, 50–4 (2011) Phipps and Cronin [2020] Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rymer, M.M.: Hemorrhagic stroke: intracerebral hemorrhage. Missouri medicine 108 1, 50–4 (2011) Phipps and Cronin [2020] Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. 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[2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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[2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rymer, M.M.: Hemorrhagic stroke: intracerebral hemorrhage. Missouri medicine 108 1, 50–4 (2011) Phipps and Cronin [2020] Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. 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[2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Phipps, M.S., Cronin, C.: Management of acute ischemic stroke. BMJ 368 (2020) Rorden et al. [2012] Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. 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NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. 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Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. 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Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., Karnath, H.-O.: Age-specific ct and mri templates for spatial normalization. NeuroImage 61, 957–965 (2012) Fiez et al. [2000] Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. 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(2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability. Human Brain Mapping 9 (2000) Ashton et al. [2003] Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Ashton, E.A., Takahashi, C., Berg, M.J., Goodman, A., Totterman, S.M.S., Ekholm, S.: Accuracy and reproducibility of manual and semiautomated quantification of ms lesions by mri. Journal of Magnetic Resonance Imaging 17 (2003) Pratiher and Chattoraj [2018] Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Pratiher, S., Chattoraj, S.: Diving deep onto discriminative ensemble of histological hashing & class-specific manifold learning for multi-class breast carcinoma taxonomy. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1025–1029 (2018) Nawn et al. [2020] Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. 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[2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Nawn, D., Pratiher, S., Chattoraj, S., Chakraborty, D., Pal, M., Paul, R.R., Dutta, S., Chatterjee, J.: Multifractal alterations in oral sub-epithelial connective tissue during progression of pre-cancer and cancer. IEEE Journal of Biomedical and Health Informatics 25, 152–162 (2020) Govindarajan et al. [2022] Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. 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(2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Govindarajan, A., Govindarajan, A., Tanamala, S., Chattoraj, S., Reddy, B., Agrawal, R., Iyer, D., Srivastava, A., Kumar, P., Putha, P.: Role of an automated deep learning algorithm for reliable screening of abnormality in chest radiographs: A prospective multicenter quality improvement study. Diagnostics 12 (2022) Pratiher et al. [2018] Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Pratiher, S., Chattoraj, S., Mukherjee, R.: Stationplot: A new non-stationarity quantification tool for detection of epileptic seizures. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 499–503 (2018) Chilamkurthy et al. [2018] Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. 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[2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. 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Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N.G., Venugopal, V.K., Mahajan, V., Rao, P., Warier, P.: Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. The Lancet 392, 2388–2396 (2018) Wilke et al. [2011] Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Wilke, M., Haan, B., Juenger, H., Karnath, H.-O.: Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods. NeuroImage 56, 2038–2046 (2011) Rekik et al. [2012] Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Rekik, I., Allassonnière, S., Carpenter, T., Wardlaw, J.M.: Medical image analysis methods in mr/ct-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. a critical appraisal. NeuroImage : Clinical 1, 164–178 (2012) Chan [2007] Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 31 4-5, 285–98 (2007) Liu et al. [2008] Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). 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(2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. 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In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. 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Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. 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Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Liu, R., Tan, C.L., Leong, T.-Y., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. 2008 19th International Conference on Pattern Recognition, 1–4 (2008) Matesin et al. [2001] Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Matesin, M., Lonvaric, S., Petravic, D.: A rule-based approach to stroke lesion analysis from ct brain images. ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 219–223 (2001) Obuchowski [1994] Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Obuchowski, N.A.: Computing sample size for receiver operating characteristic studies. Investigative Radiology 29, 238–243 (1994) Zhou et al. [2002] Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. 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Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. 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IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. 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Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). 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Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine. (2002) Clopper and Pearson [1934] Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). 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[1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Clopper, C.J., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934) DeLong et al. [1988] DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- DeLong, E.R., DeLong, D.M., Clarke‐Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 3, 837–45 (1988) Došilović et al. [2018] Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: A survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018). IEEE Adadi and Berrada [2018] Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
- Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) Gunning [2017] Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Gunning, D.: Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2) (2017) Broderick et al. [2007] Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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- Broderick, J.P., Connolly, S.E., Feldmann, E., Hanley, D.F., Kase, C.S., Krieger, D., Mayberg, M.R., Morgenstern, L.B., Ogilvy, C.S., Vespa, P.M., Zuccarello, M.: Guidelines for the management of spontaneous intracerebral hemorrhage in adults. 2007 update. guideline from the american heart association/american stroke association stroke council, high blood pressure research council, and the quality of care and outcomes in research interdisciplinary working gro. Stroke 38, 2001–2023 (2007) Srinivasan et al. [2006] Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006) Srinivasan, A., Goyal, M., Azri, F.A., Lum, C.: State-of-the-art imaging of acute stroke. Radiographics : a review publication of the Radiological Society of North America, Inc 26 Suppl 1, 75–95 (2006)
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