mlsanityData to 7 Oct 2026

Continual Learning in Medical Imaging: A Survey and Practical Analysis

arXiv 2405.13482 ↗First posted 22 May 2024cs.CVcited by 9

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Abstract (arXiv)

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 .

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 54 of its 114 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

54 of 114 references.

References · 54

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  6. 2016Learning without Forgettingcited by 2,465
  7. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  8. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  9. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,094
  10. 2021Learning to Prompt for Continual Learningcited by 938
  11. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 860
  12. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
38 more
  1. 2022DualPrompt: Complementary Prompting for Rehearsal-free Continual Learningcited by 635
  2. 2018End-to-End Incremental Learningcited by 596
  3. 2022CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learningcited by 446
  4. 2021DyTox: Transformers for Continual Learning with DYnamic TOken eXpansioncited by 291
  5. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  6. 2022Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Modelscited by 179
  7. 2020PLOP: Learning without Forgetting for Continual Semantic Segmentationcited by 177
  8. 2019Deep learning for cardiac image segmentation: A reviewcited by 131
  9. 2018A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocolscited by 49
  10. 2023Continual Learning for Abdominal Multi-Organ and Tumor Segmentationcited by 43
  11. 2020Continual Learning for Domain Adaptation in Chest X-ray Classificationcited by 31
  12. 2021Machine learning based disease diagnosis: A comprehensive reviewcited by 30
  13. 2022LifeLonger: A Benchmark for Continual Disease Classificationcited by 28
  14. 2020What is Wrong with Continual Learning in Medical Image Segmentation?cited by 25
  15. 2018Towards continual learning in medical imagingcited by 24
  16. 2022Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentationcited by 22
  17. 2020Importance Driven Continual Learning for Segmentation Across Domainscited by 21
  18. 2023Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scanscited by 21
  19. 2022Learning Incrementally to Segment Multiple Organs in a CT Imagecited by 19
  20. 2023Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseasescited by 18
  21. 2022Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whiteningcited by 16
  22. 2021Adversarial Continual Learning for Multi-Domain Hippocampal Segmentationcited by 14
  23. 2020Dynamic memory to alleviate catastrophic forgetting in continuous learning settingscited by 13
  24. 2023MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Partscited by 13
  25. 2023Fourier Test-time Adaptation with Multi-level Consistency for Robust Classificationcited by 12
  26. 2019Towards continuous learning for glioma segmentation with elastic weight consolidationcited by 11
  27. 2022Continual Hippocampus Segmentation with Transformerscited by 11
  28. 2018Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networkscited by 9
  29. 2023Leveraging Old Knowledge to Continually Learn New Classes in Medical Imagescited by 9
  30. 2023Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRIcited by 9
  31. 2023Generative appearance replay for continual unsupervised domain adaptationcited by 8
  32. 2022Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sitescited by 7
  33. 2022Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shiftscited by 6
  34. 2023Multi-scale Feature Alignment for Continual Learning of Unlabeled Domainscited by 6
  35. 2023L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Spacecited by 6
  36. 2024Multi-Label Continual Learning for the Medical Domain: A Novel Benchmarkcited by 6
  37. 2024DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Imagescited by 6
  38. 2022Class Impression for Data-free Incremental Learningcited by 4

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentationcited by 3
  2. 2025EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learningcited by 2
  3. 2025MedPEFT-CL: Dual-Phase Parameter-Efficient Continual Learning with Medical Semantic Adapter and Bidirectional Memory Consolidationcited by 1
  4. 2026Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domainscited by 1
  5. 2025UniPrompt-CL: Sustainable Continual Learning in Medical AI with Unified Prompt Poolscited by 0
  6. 2025Data Efficiency and Transfer Robustness in Biomedical Image Segmentation: A Study of Redundancy and Forgetting with Cellposecited by 0
  7. 2026Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AIcited by 0
  8. 2026CADRE: Stable, Parameter Efficient Adaptation of Medical Vision Language Models with Bounded Forgetting and Prior Driftcited by 0
  9. 2026An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answeringcited by 0