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arXiv · 2509.23906

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

Anoushka Harit·William Prew·Zhongtian Sun·Florian Markowetz
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Abstract

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 DER\texttt{++}, 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.

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Anoushka Harit, William Prew, Zhongtian Sun, Florian Markowetz. 2025-09-28. EWC-Guided Diffusion Replay for Exemplar-Free Continual Learning in Medical Imaging. https://arxiv.org/abs/2509.23906

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