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Continual Learning in Medical Imaging: A Survey and Practical Analysis (2405.13482v2)

Published 22 May 2024 in cs.CV

Abstract: Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distribution shifts in the continuously evolving data stream increase the gap between research and applications. Continual Learning offers promise in addressing these hurdles by enabling the sequential acquisition of new knowledge without forgetting previous learnings in neural networks. In this survey, we comprehensively review the recent literature on continual learning in the medical domain, highlight recent trends, and point out the practical issues. Specifically, we survey the continual learning studies on classification, segmentation, detection, and other tasks in the medical domain. Furthermore, we develop a taxonomy for the reviewed studies, identify the challenges, and provide insights to overcome them. We also critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions. We hope this survey will provide researchers with a useful overview of the developments in the field and will further increase interest in the community. To keep up with the fast-paced advancements in this field, we plan to routinely update the repository with the latest relevant papers at https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical .

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References (95)
  1. Machine learning in clinical decision making. Med 2, 642–665.
  2. Machine-learning-based disease diagnosis: A comprehensive review, in: Healthcare, MDPI. p. 541.
  3. Manifold learning to address catastrophic forgetting, in: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 1–5.
  4. Memory aware synapses: Learning what (not) to forget, in: Proceedings of the European conference on computer vision (ECCV), pp. 139–154.
  5. Class impression for data-free incremental learning, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 320–329.
  6. Towards continual learning in medical imaging. arXiv preprint arXiv:1811.02496 .
  7. Continual-gen: Continual group ensembling for domain-agnostic skin lesion classification, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 3–13.
  8. Culprit-prune-net: Efficient continual sequential multi-domain learning with application to skin lesion classification, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 165–175.
  9. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy, in: Proceedings of the 2020 CHI conference on human factors in computing systems, pp. 1–12.
  10. Memory replay for continual medical image segmentation through atypical sample selection, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 513–522.
  11. End-to-end incremental learning, in: Proceedings of the European conference on computer vision (ECCV), pp. 233–248.
  12. Contrastive representations for continual learning of fine-grained histology images, in: Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, Springer. pp. 1–9.
  13. Leveraging old knowledge to continually learn new classes in medical images. arXiv preprint arXiv:2303.13752 .
  14. Generative appearance replay for continual unsupervised domain adaptation. arXiv preprint arXiv:2301.01211 .
  15. Deep learning for cardiac image segmentation: a review. Frontiers in Cardiovascular Medicine 7, 25.
  16. Targeted gradient descent: A novel method for convolutional neural networks fine-tuning and online-learning, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III 24, Springer. pp. 25–35.
  17. Breast cancer detection model training strategy based on continual learning, in: CAIBDA 2022; 2nd International Conference on Artificial Intelligence, Big Data and Algorithms, VDE. pp. 1–5.
  18. Problems in the deployment of machine-learned models in health care. CMAJ 193, E1391–E1394.
  19. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine 24, 1342–1350.
  20. Lifelonger: A benchmark for continual disease classification, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 314–324.
  21. Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models. arXiv preprint arXiv:2203.06904 .
  22. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 .
  23. Plop: Learning without forgetting for continual semantic segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4040–4050.
  24. Dytox: Transformers for continual learning with dynamic token expansion, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9285–9295.
  25. Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking. Computerized Medical Imaging and Graphics 109, 102290.
  26. Towards continuous learning for glioma segmentation with elastic weight consolidation. arXiv preprint arXiv:1909.11479 .
  27. Task-agnostic continual hippocampus segmentation for smooth population shifts, in: MICCAI Workshop on Domain Adaptation and Representation Transfer, Springer. pp. 108–118.
  28. What is wrong with continual learning in medical image segmentation? arXiv preprint arXiv:2010.11008 .
  29. Lung nodule detection from feature engineering to deep learning in thoracic ct images: a comprehensive review. Journal of digital imaging 33, 655–677.
  30. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.
  31. Incremental learning for exudate and hemorrhage segmentation on fundus images. Information Fusion 73, 157–164.
  32. Dynamic memory to alleviate catastrophic forgetting in continuous learning settings, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23, Springer. pp. 359–368.
  33. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental 4, 1–13.
  34. Parameter-efficient transfer learning for nlp, in: International Conference on Machine Learning, PMLR. pp. 2790–2799.
  35. Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708.
  36. Fourier test-time adaptation with multi-level consistency for robust classification. arXiv preprint arXiv:2306.02544 .
  37. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18, 203–211.
  38. Continual segment: Towards a single, unified and non-forgetting continual segmentation model of 143 whole-body organs in ct scans, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21140–21151.
  39. Reliability in multi-site structural mri studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30, 436–443.
  40. A lifelong learning approach to brain mr segmentation across scanners and protocols, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 476–484.
  41. Keep and learn: Continual learning by constraining the latent space for knowledge preservation in neural networks, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, Springer. pp. 520–528.
  42. Continual learning framework for a multicenter study with an application to electrocardiogram. BMC Medical Informatics and Decision Making 24, 67.
  43. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114, 3521–3526.
  44. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25.
  45. Medical images classification using deep learning: a survey. Multimedia Tools and Applications doi:10.1007/s11042-023-15576-. published online.
  46. Continual learning for domain adaptation in chest x-ray classification, in: Medical Imaging with Deep Learning, PMLR. pp. 413–423.
  47. Domain-incremental cardiac image segmentation with style-oriented replay and domain-sensitive feature whitening. IEEE Transactions on Medical Imaging 42, 570–581.
  48. Class-incremental gesture recognition learning with out-of-distribution detection, in: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. pp. 1503–1508.
  49. Continual learning of medical image classification based on feature replay, in: 2022 16th IEEE International Conference on Signal Processing (ICSP), IEEE. pp. 426–430.
  50. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40, 2935–2947.
  51. Continual learning of new diseases with dual distillation and ensemble strategy, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23, Springer. pp. 169–178.
  52. Muscle: Multi-task self-supervised continual learning to pre-train deep models for x-ray images of multiple body parts, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 151–161.
  53. A new generative replay approach for incremental class learning of medical image for semantic segmentation, in: Proceedings of the 2022 International Conference on Intelligent Medicine and Health, pp. 51–56.
  54. Learning incrementally to segment multiple organs in a ct image, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 714–724.
  55. Incremental learning for heterogeneous structure segmentation in brain tumor mri, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 46–56.
  56. Incremental learning for dermatological imaging modality classification. Journal of Imaging 7, 180.
  57. Resilience-aware mlops for ai-based medical diagnostic system. Frontiers in Public Health 12, 1342937.
  58. Idt: An incremental deep tree framework for biological image classification. Artificial Intelligence in Medicine 134, 102392.
  59. Importance driven continual learning for segmentation across domains, in: Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 11, Springer. pp. 423–433.
  60. Multimodal continual learning with sonographer eye-tracking in fetal ultrasound, in: Simplifying Medical Ultrasound: Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 2, Springer. pp. 14–24.
  61. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nature communications 12, 5678.
  62. Common mri acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial mri. Neuroimage 30, 1196–1202.
  63. Multi-task learning approach for unified biometric estimation from fetal ultrasound anomaly scans, in: International Conference on Medical Imaging and Computer-Aided Diagnosis, Springer. pp. 52–61.
  64. Dynammo: Dynamic model merging for efficient class incremental learning for medical images. arXiv preprint arXiv:2404.14099 .
  65. Continual learning for medical image classification, in: CEUR Workshop Proceedings (CEUR-WS. org).
  66. Continual hippocampus segmentation with transformers, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3711–3720.
  67. Feature transformers: privacy preserving lifelong learners for medical imaging, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV 22, Springer. pp. 347–355.
  68. icarl: Incremental classifier and representation learning, in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2001–2010.
  69. L3dmc: Lifelong learning using distillation via mixed-curvature space, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 123–133.
  70. f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis 54, 30–44.
  71. Privacy preserving synthetic respiratory sounds for class incremental learning. Smart Health 23, 100232.
  72. Replay-oriented gradient projection memory for continual learning in medical scenarios, in: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 1724–1729.
  73. Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11909–11919.
  74. Continual domain incremental learning for chest x-ray classification in low-resource clinical settings, in: MICCAI Workshop on Domain Adaptation and Representation Transfer, Springer. pp. 226–238.
  75. Multi-scale feature alignment for continual learning of unlabeled domains. arXiv:2302.01287.
  76. Multi-scale multi-task distillation for incremental 3d medical image segmentation, in: European Conference on Computer Vision, Springer. pp. 369--384.
  77. Three types of incremental learning. Nature Machine Intelligence 4, 1185--1197.
  78. Towards continuous domain adaptation for medical imaging, in: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE. pp. 443--446.
  79. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. The Lancet Respiratory Medicine 6, 837--845.
  80. A comprehensive survey of continual learning: Theory, method and application. arXiv preprint arXiv:2302.00487 .
  81. Incremental learning method for lung nodule detection based on ewc and feature distillation, in: International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), SPIE. pp. 868--874.
  82. Lifelong learning based disease diagnosis on clinical notes, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 213--224.
  83. Dualprompt: Complementary prompting for rehearsal-free continual learning, in: European Conference on Computer Vision, Springer. pp. 631--648.
  84. Learning to prompt for continual learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 139--149.
  85. Representative data selection for efficient medical incremental learning, in: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1--4. doi:10.1109/EMBC40787.2023.10341107.
  86. Task-incremental medical image classification with task-specific batch normalization, in: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Springer. pp. 309--320.
  87. Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis, in: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 191--195.
  88. Incremental learning meets transfer learning: Application to multi-site prostate mri segmentation, in: International Workshop on Distributed, Collaborative, and Federated Learning, Springer. pp. 3--16.
  89. Comprehensive importance-based selective regularization for continual segmentation across multiple sites, in: Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part I 24, Springer. pp. 389--399.
  90. S 3 r: Shape and semantics-based selective regularization for explainable continual segmentation across multiple sites. IEEE Transactions on Medical Imaging .
  91. Learning towards synchronous network memorizability and generalizability for continual segmentation across multiple sites, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 380--390.
  92. Adapter learning in pretrained feature extractor for continual learning of diseases. arXiv preprint arXiv:2304.09042 .
  93. Continual learning for abdominal multi-organ and tumor segmentation, in: International conference on medical image computing and computer-assisted intervention, Springer. pp. 35--45.
  94. Class-specific prompts in vision transformer for continual learning of new diseases, in: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 994--999.
  95. Deep class-incremental learning: A survey. arXiv preprint arXiv:2302.03648 .
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Authors (7)
  1. Mohammad Areeb Qazi (8 papers)
  2. Anees Ur Rehman Hashmi (8 papers)
  3. Santosh Sanjeev (10 papers)
  4. Ibrahim Almakky (21 papers)
  5. Numan Saeed (24 papers)
  6. Mohammad Yaqub (77 papers)
  7. Camila Gonzalez (40 papers)

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