Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Depth-induced Saliency Comparison Network for Diagnosis of Alzheimer's Disease via Jointly Analysis of Visual Stimuli and Eye Movements (2403.10124v1)

Published 15 Mar 2024 in cs.CV

Abstract: Early diagnosis of Alzheimer's Disease (AD) is very important for following medical treatments, and eye movements under special visual stimuli may serve as a potential non-invasive biomarker for detecting cognitive abnormalities of AD patients. In this paper, we propose an Depth-induced saliency comparison network (DISCN) for eye movement analysis, which may be used for diagnosis the Alzheimers disease. In DISCN, a salient attention module fuses normal eye movements with RGB and depth maps of visual stimuli using hierarchical salient attention (SAA) to evaluate comprehensive saliency maps, which contain information from both visual stimuli and normal eye movement behaviors. In addition, we introduce serial attention module (SEA) to emphasis the most abnormal eye movement behaviors to reduce personal bias for a more robust result. According to our experiments, the DISCN achieves consistent validity in classifying the eye movements between the AD patients and normal controls.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (70)
  1. T. T. L. Vuong, B. Song, K. Kim, Y. M. Cho, and J. T. Kwak, “Multi-scale binary pattern encoding network for cancer classification in pathology images,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 3, pp. 1152–1163, 2022.
  2. S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I. S. Kweon, and S. Xie, “Convnext v2: Co-designing and scaling convnets with masked autoencoders,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023, pp. 16 133–16 142.
  3. B. Dubois, A. Padovani, P. Scheltens, A. Rossi, and G. Dell’Agnello, “Timely diagnosis for alzheimer’s disease: a literature review on benefits and challenges,” Journal of Alzheimer’s Disease, vol. 49, no. 3, pp. 617–631, 2016.
  4. Q. Zhou, M. Goryawala, M. Cabrerizo, W. Barker, D. Loewenstein, R. Duara, and M. Adjouadi, “Multivariate analysis of structural mri and pet (fdg and 18f-av-45) for alzheimer’s disease and its prodromal stages,” in Proceedings of the IEEE Conference on the Engineering in Medicine and Biology Society, 2014, pp. 1051–1054.
  5. D. Chen, A. Alsadoon, P. Prasad, and A. Elchouemi, “Early diagnosis of alzheimer using mini mental state examination method: Mmse,” in Proceedings of the IEEE Conference on Information and Communication Systems, 2017, pp. 125–129.
  6. C. S. Eke, E. Jammeh, X. Li, C. Carroll, S. Pearson, and E. Ifeachor, “Early detection of alzheimer’s disease with blood plasma proteins using support vector machines,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 1, pp. 218–226, 2020.
  7. G. Palacios-Navarro, J. Buele, S. G. Jarque, and A. B. García, “Cognitive decline detection for alzheimer’s disease patients through an activity of daily living (adl),” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2225–2232, 2022.
  8. M. R. Readman, M. Polden, M. C. Gibbs, L. Wareing, and T. J. Crawford, “The potential of naturalistic eye movement tasks in the diagnosis of alzheimer’s disease: a review,” Brain Sciences, vol. 11, no. 11, p. 1503, 2021.
  9. N. Noiret, B. Vigneron, M. Diogo, P. Vandel, and É. Laurent, “Saccadic eye movements: what do they tell us about aging cognition?” Aging, Neuropsychology, and Cgnition, vol. 24, no. 5, pp. 575–599, 2017.
  10. A. Coors, M.-A. Imtiaz, M. M. Boenniger, N. A. Aziz, U. Ettinger, and M. M. Breteler, “Associations of genetic liability for alzheimer’s disease with cognition and eye movements in a large, population-based cohort study,” Translational Psychiatry, vol. 12, no. 1, p. 337, 2022.
  11. J. Liu, Y. Pan, F.-X. Wu, and J. Wang, “Enhancing the feature representation of multi-modal mri data by combining multi-view information for mci classification,” Neurocomputing, vol. 400, pp. 322–332, 2020.
  12. J. Opwonya, D. N. T. Doan, S. G. Kim, J. I. Kim, B. Ku, S. Kim, S. Park, and J. U. Kim, “Saccadic eye movement in mild cognitive impairment and alzheimer’s disease: a systematic review and meta-analysis,” Neuropsychology Review, vol. 32, no. 2, pp. 193–227, 2022.
  13. M. A. Parra, J. Granada, and G. Fernández, “Memory-driven eye movements prospectively predict dementia in people at risk of alzheimer’s disease,” Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 14, no. 1, p. e12386, 2022.
  14. E. Costanzo, I. Lengyel, M. Parravano, I. Biagini, M. Veldsman, A. Badhwar, M. Betts, A. Cherubini, D. J. Llewellyn, I. Lourida et al., “Ocular biomarkers for alzheimer disease dementia: An umbrella review of systematic reviews and meta-analyses,” JAMA Ophthalmology, 2022.
  15. H. Ramzaoui, S. Faure, R. David, and S. Spotorno, “Top-down and bottom-up sources of eye-movement guidance during realistic scene search in alzheimer’s disease.” Neuropsychology, 2022.
  16. A. K. Malik, M. Ganaie, M. Tanveer, P. Suganthan, A. D. N. I. Initiative et al., “Alzheimer’s disease diagnosis via intuitionistic fuzzy random vector functional link network,” IEEE Transactions on Computational Social Systems, 2022.
  17. R. Shi, L. Wang, J. Jiang, A. D. N. Initiative et al., “An unsupervised region of interest extraction model for tau pet images and its application in the diagnosis of alzheimer’s disease,” in Proceedings of the IEEE Conference on Engineering in Medicine & Biology Society, 2022, pp. 2157–2160.
  18. B. S. S. Varma, G. Kalyani, K. Asish, and M. I. Bai, “Early detection of alzheimer’s disease using svm, random forest & fnn algorithms,” in Proceedings of the IEEE Conference on Innovation in Technology, 2023, pp. 1–6.
  19. J. Sun, Y. Liu, H. Wu, P. Jing, and Y. Ji, “A novel deep learning approach for diagnosing alzheimer’s disease based on eye-tracking data,” Frontiers in Human Neuroscience, 2022.
  20. Y. Yin, H. Wang, S. Liu, J. Sun, P. Jing, and Y. Liu, “Internet of things for diagnosis of alzheimer’s disease: A multimodal machine learning approach based on eye movement features,” IEEE Internet of Things Journal, 2023.
  21. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.
  22. J. Xu, Y. Pan, X. Pan, S. Hoi, Z. Yi, and Z. Xu, “Regnet: self-regulated network for image classification,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
  23. S. Mehta and M. Rastegari, “Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer,” arXiv preprint arXiv:2110.02178, 2021.
  24. T. T. Vuong, B. Song, K. Kim, Y. M. Cho, and J. T. Kwak, “Multi-scale binary pattern encoding network for cancer classification in pathology images,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 3, pp. 1152–1163, 2021.
  25. Z. S. Nasreddine, N. A. Phillips, V. Bédirian, S. Charbonneau, V. Whitehead, I. Collin, J. L. Cummings, and H. Chertkow, “The montreal cognitive assessment, moca: a brief screening tool for mild cognitive impairment,” Journal of the American Geriatrics Society, vol. 53, no. 4, pp. 695–699, 2005.
  26. J. Sun, Z. Wu, H. Wang, P. Jing, and Y. Liu, “A novel integrated eye-tracking system with stereo stimuli for 3-d gaze estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–15, 2023.
  27. K. O’Shea and R. Nash, “An introduction to convolutional neural networks,” arXiv preprint arXiv:1511.08458, 2015.
  28. S. A. Huettel and G. McCarthy, “What is odd in the oddball task?: Prefrontal cortex is activated by dynamic changes in response strategy,” Neuropsychologia, vol. 42, no. 3, pp. 379–386, 2004.
  29. C.-F. Tsai, C.-C. Chen, E. H.-K. Wu, C.-R. Chung, C.-Y. Huang, P.-Y. Tsai, and S.-C. Yeh, “A machine-learning-based assessment method for early-stage neurocognitive impairment by an immersive virtual supermarket,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2124–2132, 2021.
  30. E. H. Singleton, J. L. Fieldhouse, J. J. van’t Hooft, M. Scarioni, M.-P. E. van Engelen, S. A. Sikkes, C. de Boer, D. I. Bocancea, E. van den Berg, P. Scheltens et al., “Social cognition deficits and biometric signatures in the behavioural variant of alzheimer’s disease,” Brain, vol. 146, no. 5, pp. 2163–2174, 2023.
  31. A. W. Przybyszewski, A. Śledzianowski, A. Chudzik, S. Szlufik, and D. Koziorowski, “Machine learning and eye movements give insights into neurodegenerative disease mechanisms,” Sensors, vol. 23, no. 4, p. 2145, 2023.
  32. R. U. Haque, A. L. Pongos, C. M. Manzanares, J. J. Lah, A. I. Levey, and G. D. Clifford, “Deep convolutional neural networks and transfer learning for measuring cognitive impairment using eye-tracking in a distributed tablet-based environment,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 1, pp. 11–18, 2020.
  33. V. Vinayak, M. Paliwal, J. Amudha, and C. Jyotsna, “Prediction of neuro cognitive disorders using supervised comparative machine learning model & scanpath representations,” in Proceedings of the IEEE Conference on Convergence in Technology, 2023, pp. 1–5.
  34. A. Tales, J. Muir, R. Jones, A. Bayer, and R. J. Snowden, “The effects of saliency and task difficulty on visual search performance in ageing and alzheimer’s disease,” Neuropsychologia, vol. 42, no. 3, pp. 335–345, 2004.
  35. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254–1259, 1998.
  36. W. M. Association et al., “World medical association declaration of helsinki. ethical principles for medical research involving human subjects.” Bulletin of the World Health Organization, vol. 79, no. 4, p. 373, 2001.
  37. R. J. Molitor, P. C. Ko, and B. A. Ally, “Eye movements in alzheimer’s disease,” Journal of Alzheimer’s disease, vol. 44, no. 1, pp. 1–12, 2015.
  38. H. Eraslan Boz, K. Koçoğlu, M. Akkoyun, I. Y. Tüfekci, M. Ekin, P. Özçelik, and G. Akdal, “Uncorrected errors and correct saccades in the antisaccade task distinguish between early-stage alzheimer’s disease dementia, amnestic mild cognitive impairment, and normal aging,” Aging, Neuropsychology, and Cognition, pp. 1–22, 2023.
  39. S. Hannonen, S. Andberg, V. Kärkkäinen, M. Rusanen, J.-M. Lehtola, T. Saari, V. Korhonen, L. Hokkanen, M. Hallikainen, T. Hänninen et al., “Shortening of saccades as a possible easy-to-use biomarker to detect risk of alzheimer’s disease,” Journal of Alzheimer’s Disease, vol. 88, no. 2, pp. 609–618, 2022.
  40. H. Eraslan Boz, K. Koçoğlu, M. Akkoyun, I. Y. Tüfekci, M. Ekin, P. Özçelik, and G. Akdal, “The influence of stimulus eccentricity on prosaccade outcomes in patients with alzheimer’s disease dementia at an early stage and amnestic mild cognitive impairment,” Journal of Clinical and Experimental Neuropsychology, vol. 44, no. 10, pp. 713–729, 2022.
  41. S.-i. Tokushige, H. Matsumoto, S.-i. Matsuda, S. Inomata-Terada, N. Kotsuki, M. Hamada, S. Tsuji, Y. Ugawa, and Y. Terao, “Early detection of cognitive decline in alzheimer’s disease using eye tracking,” Frontiers in Aging Neuroscience, vol. 15, p. 1123456, 2023.
  42. H. Jang, T. Soroski, M. Rizzo, O. Barral, A. Harisinghani, S. Newton-Mason, S. Granby, T. M. Stutz da Cunha Vasco, C. Lewis, P. Tutt et al., “Classification of alzheimer’s disease leveraging multi-task machine learning analysis of speech and eye-movement data,” Frontiers in Human Neuroscience, vol. 15, p. 716670, 2021.
  43. Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48–62, 2021.
  44. D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473, 2014.
  45. V. Mnih, N. Heess, A. Graves et al., “Recurrent models of visual attention,” Advances in neural information processing systems, vol. 27, 2014.
  46. M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025, 2015.
  47. M. Jaderberg, K. Simonyan, A. Zisserman et al., “Spatial transformer networks,” Advances in Neural Information Processing Systems, vol. 28, 2015.
  48. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132–7141.
  49. L. Liu, M. Utiyama, A. Finch, and E. Sumita, “Neural machine translation with supervised attention,” arXiv preprint arXiv:1609.04186, 2016.
  50. H. Mi, Z. Wang, and A. Ittycheriah, “Supervised attentions for neural machine translation,” arXiv preprint arXiv:1608.00112, 2016.
  51. O. Barral, H. Jang, S. Newton-Mason, S. Shajan, T. Soroski, G. Carenini, C. Conati, and T. Field, “Non-invasive classification of alzheimer’s disease using eye tracking and language,” in Proceedings of the 5th Machine Learning for Healthcare Conference, ser. Proceedings of Machine Learning Research, F. Doshi-Velez, J. Fackler, K. Jung, D. Kale, R. Ranganath, B. Wallace, and J. Wiens, Eds., vol. 126, 2020, pp. 813–841.
  52. M. L. G. d. F. Pereira, M. v. Z. d. A. Camargo, A. F. R. Bellan, A. C. Tahira, B. Dos Santos, J. Dos Santos, A. Machado-Lima, F. L. Nunes, and O. V. Forlenza, “Visual search efficiency in mild cognitive impairment and alzheimer’s disease: An eye movement study,” Journal of Alzheimer’s Disease, vol. 75, no. 1, pp. 261–275, 2020.
  53. O. Barral, H. Jang, S. Newton-Mason, S. Shajan, T. Soroski, G. Carenini, C. Conati, and T. Field, “Non-invasive classification of alzheimer’s disease using eye tracking and language,” in Machine Learning for Healthcare Conference, 2020, pp. 813–841.
  54. D. González-Zúñiga, A. Chistyakov, P. Orero, and J. Carrabina, “Breaking the pattern: Study on stereoscopic web perception,” in Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction: 7th International Conference, UCAmI 2013, Carrillo, Costa Rica, December 2-6, 2013, Proceedings, 2013, pp. 26–33.
  55. R. Caldara and S. Miellet, “i map: A novel method for statistical fixation mapping of eye movement data,” Behavior Research Methods, vol. 43, pp. 864–878, 2011.
  56. L. Prechelt, “Automatic early stopping using cross validation: quantifying the criteria,” Neural Networks, vol. 11, no. 4, pp. 761–767, 1998.
  57. K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE Conference on Computer Vision, 2015, pp. 1026–1034.
  58. N. Liu, N. Zhang, K. Wan, L. Shao, and J. Han, “Visual saliency transformer,” in Proceedings of the IEEE Conference on Computer Vision, 2021, pp. 4722–4732.
  59. L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, and S. Yan, “Tokens-to-token vit: Training vision transformers from scratch on imagenet,” in Proceedings of the IEEE conference on computer vision, 2021, pp. 558–567.
  60. G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, and E. M. Stadlan, “Clinical diagnosis of alzheimer’s disease: Report of the nincds-adrda work group* under the auspices of department of health and human services task force on alzheimer’s disease,” Neurology, vol. 34, no. 7, pp. 939–939, 1984.
  61. L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z. Jiang, F. E. H. Tay, J. Feng, and S. Yan, “Tokens-to-token vit: Training vision transformers from scratch on imagenet,” in Proceedings of the IEEE Conference on Computer Vision, 2021, pp. 558–567.
  62. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Neural Information Processing Systems, vol. 30, pp. 5998–6008, 2017.
  63. J. Ngiam, Z. Chen, D. Chia, P. Koh, Q. Le, and A. Ng, “Tiled convolutional neural networks,” Advances in Neural Information Processing Ssystems, vol. 23, 2010.
  64. X. Ding, X. Zhang, J. Han, and G. Ding, “Scaling up your kernels to 31x31: Revisiting large kernel design in cnns,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2022, pp. 11 963–11 975.
  65. Q. Zhao, Y. Huang, W. Hu, F. Zhang, and J. Liu, “Mixpro: Data augmentation with maskmix and progressive attention labeling for vision transformer,” in Proceedings of International Conference on Learning Representations, 2023.
  66. M. Dehghani, J. Djolonga, B. Mustafa, P. Padlewski, J. Heek, J. Gilmer, A. P. Steiner, M. Caron, R. Geirhos, I. Alabdulmohsin et al., “Scaling vision transformers to 22 billion parameters,” in Proceedings of International Conference on Machine Learning.   PMLR, 2023, pp. 7480–7512.
  67. X. Shen, Y. Wang, M. Lin, Y. Huang, H. Tang, X. Sun, and Y. Wang, “Deepmad: Mathematical architecture design for deep convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023, pp. 6163–6173.
  68. L. Zhu, X. Wang, Z. Ke, W. Zhang, and R. W. Lau, “Biformer: Vision transformer with bi-level routing attention,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 10 323–10 333.
  69. Y. Fang, W. Wang, B. Xie, Q. Sun, L. Wu, X. Wang, T. Huang, X. Wang, and Y. Cao, “Eva: Exploring the limits of masked visual representation learning at scale,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 19 358–19 369.
  70. W. Yu, C. Si, P. Zhou, M. Luo, Y. Zhou, J. Feng, S. Yan, and X. Wang, “Metaformer baselines for vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 2, pp. 896–912, 2023.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now