Continual Learning for Heterogeneous Medical VQA: An Empirical Analysis

Jul 15, 2026

A new preprint systematically evaluates continual learning (CL) methods for medical visual question answering (MedVQA) across a range of clinical tasks, including classification, detection, cell counting, and report generation. The study finds that current CL methods have difficulty maintaining a balance between retaining old knowledge and learning new tasks when faced with diverse objectives and supervision formats. The authors also analyze the impact of task ordering and the evolution of model parameters during continual learning. Code and experimental setup will be made publicly available.

Why it matters: This work exposes key limitations in current continual learning approaches for medical VQA, highlighting challenges that must be addressed for robust real-world deployment.

Full story at: arXiv Computer Vision ↗