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Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities (2307.13986v2)

Published 26 Jul 2023 in eess.IV and cs.CV

Abstract: Purpose: Manual annotations for training deep learning (DL) models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples. Methods: The experiments are performed on two lower extremity (LE) datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using Dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. Results: In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8\% Dice and 1.0\% RAC increase in CT (statistically significant), and a 0.8\% Dice and 1.1\% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone. Conclusion: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.

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References (25)
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In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ogawa T, Takao M, Otake Y, Yokota F, Hamada H, Sakai T, Sato Y, Sugano N (2020) Validation study of the ct-based cross-sectional evaluation of muscular atrophy and fatty degeneration around the pelvis and the femur. Journal of Orthopaedic Science 25(1):139–144 Yagi et al [2022] Yagi M, Taniguchi M, Tateuchi H, Hirono T, Fukumoto Y, Yamagata M, Nakai R, Yamada Y, Kimura M, Ichihashi N (2022) Age-and sex-related differences of muscle cross-sectional area in iliocapsularis: a cross-sectional study. BMC geriatrics 22(1):435 Sourati et al [2018] Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yagi M, Taniguchi M, Tateuchi H, Hirono T, Fukumoto Y, Yamagata M, Nakai R, Yamada Y, Kimura M, Ichihashi N (2022) Age-and sex-related differences of muscle cross-sectional area in iliocapsularis: a cross-sectional study. BMC geriatrics 22(1):435 Sourati et al [2018] Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  2. Ogawa T, Takao M, Otake Y, Yokota F, Hamada H, Sakai T, Sato Y, Sugano N (2020) Validation study of the ct-based cross-sectional evaluation of muscular atrophy and fatty degeneration around the pelvis and the femur. Journal of Orthopaedic Science 25(1):139–144 Yagi et al [2022] Yagi M, Taniguchi M, Tateuchi H, Hirono T, Fukumoto Y, Yamagata M, Nakai R, Yamada Y, Kimura M, Ichihashi N (2022) Age-and sex-related differences of muscle cross-sectional area in iliocapsularis: a cross-sectional study. BMC geriatrics 22(1):435 Sourati et al [2018] Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yagi M, Taniguchi M, Tateuchi H, Hirono T, Fukumoto Y, Yamagata M, Nakai R, Yamada Y, Kimura M, Ichihashi N (2022) Age-and sex-related differences of muscle cross-sectional area in iliocapsularis: a cross-sectional study. BMC geriatrics 22(1):435 Sourati et al [2018] Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. 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In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. 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In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. 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In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. 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In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. 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In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. 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IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA, Springer, pp 83–91 Settles and Craven [2008] Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
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In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079 Budd et al [2021] Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. 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Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. 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Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  6. Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71:102062 Gal and Ghahramani [2016] Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  7. Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning, PMLR, pp 1050–1059 Gal et al [2017] Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  8. Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. In: International conference on machine learning, PMLR, pp 1183–1192 Lakshminarayanan et al [2017] Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  9. Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 Gaillochet et al [2023] Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  10. Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Medical Image Analysis p 102958 Yang et al [2017] Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  11. Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: MICCAI, Quebec City, QC, Canada, Springer, pp 399–407 Hiasa et al [2019] Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE transactions on medical imaging 39(4):1030–1040 Ozdemir et al [2021] Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. 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Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. 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Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  13. Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on bayesian sample queries. Knowledge-Based Systems 214:106531 Smailagic et al [2018] Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  14. Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: Accurate and robust deep active learning for medical image analysis. In: ICMLA, IEEE, pp 481–488 Nath et al [2020] Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  15. Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: Active learning for medical image segmentation. IEEE Transactions on Medical Imaging 40(10):2534–2547 Li et al [2023] Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  16. Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) Hal-ia: A hybrid active learning framework using interactive annotation for medical image segmentation. Medical Image Analysis p 102862 Liu et al [2022] Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  17. Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Computing Surveys (CSUR) 54(10s):1–34 Amagata [2023] Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  18. Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12338–12345 Fukumoto et al [2022] Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  19. Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle & Nerve 66(5):568–575 Rosset et al [2004] Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  20. Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. Journal of digital imaging 17:205–216 Kikinis et al [2013] Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  21. Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, p 277–289 Lin et al [2017] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  22. Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988 Chen et al [2023] Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  23. Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: Exploiting data redundancy for optimization of deep learning. ACM Computing Surveys 55(10):1–38 Yuan et al [2019] Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  24. Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowledge-Based Systems 172:86–94 Nath et al [2022] Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308 Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
  25. Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI, Singapore, Springer, pp 297–308
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