2025/09/28 by Anoushka Harit, Harit, Anoushka, William Prew +5
Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fidelity #Forgetting #Geophysical Methods and Applications #High fidelity #Medical imaging #Microwave Imaging and Scattering Analysis #Retraining #Set (abstract data type)
paper · pdf · doi:10.48550/arxiv.2509.23906
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/09/28 · openalex created_date 2025/10/19 · openalex updated_date 2026/08/05
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++, 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.