An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge. Continual learning (CL) provides a practical framework for this setting. Despite rapid progress in medical vision-language models, the behavior of CL methods when training these models across heterogeneous MedVQA tasks remains underexplored. This work presents a systematic evaluation of CL for MedVQA across diverse clinical objectives, including classification, multi-label classification, detection, cell counting, and report generation. Specifically, we explore (1) the ability of existing CL methods ...
| Authors | Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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