mlsanityData to 7 Oct 2026

EWC-Guided Diffusion Replay for Exemplar-Free Continual Learning in Medical Imaging

arXiv 2509.23906 ↗First posted 28 Sep 2025cs.CV cs.AIcited by 0

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

Medical imaging foundation models must adapt over time, yet full retraining is often blocked by privacy constraints and cost. We present a continual learning framework that avoids storing patient exemplars by pairing class conditional diffusion replay with Elastic Weight Consolidation. Using a compact Vision Transformer backbone, we evaluate across eight MedMNIST v2 tasks and CheXpert. On CheXpert our approach attains 0.851 AUROC, reduces forgetting by more than 30% relative to DERtexttt++, and approaches joint training at 0.869 AUROC, while remaining efficient and privacy preserving. Analyses connect forgetting to two measurable factors: fidelity of replay and Fisher weighted parameter drift, highlighting the complementary roles of replay diffusion and synaptic stability. The results indicate a practical route for scalable, privacy aware continual adaptation of clinical imaging models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 20 of its 27 references that are in the map. First posted on arXiv 28 Sep 2025.

Builds on

20 of 27 references.

References · 20

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
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  6. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  7. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  8. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  9. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  10. 2017Continual Learning with Deep Generative Replaycited by 1,152
  11. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,094
  12. 2020Dark Experience for General Continual Learning: a Strong, Simple Baselinecited by 895
8 more
  1. 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 734
  2. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  3. 2018Understanding disentangling in β-VAEcited by 332
  4. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  5. 2020Continual Prototype Evolution: Learning Online from Non-Stationary Data Streamscited by 143
  6. 2018Banach Wasserstein GANcited by 56
  7. 2024PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learningcited by 8
  8. 2025Information-Theoretic Dual Memory System for Continual Learningcited by 1

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  1. 2025Avoid Catastrophic Forgetting with Rank-1 Fisher from Diffusion Models
  2. 2025Prompt-Aware Adaptive Elastic Weight Consolidation for Continual Learning in Medical Vision-Language Models
  3. 2023Exploring Continual Learning of Diffusion Models
  4. 2023Continual Learning of Diffusion Models with Generative Distillation
  5. 2024Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning
  6. 2024Joint Diffusion models in Continual Learning
  7. 2025Locality-Aware Continual Unlearning for Diffusion Models
  8. 2023Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning