An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
This paper presents a systematic evaluation of continual learning methods for heterogeneous medical visual question answering tasks, including classification, multi-label classification, detection, cell counting, and report generation. Findings show existing methods struggle to maintain stability-plasticity balance when tasks with different objectives are interleaved.
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[Submitted on 13 Jul 2026]
Title:An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
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Abstract: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 to mitigate catastrophic forgetting; (2) their sensitivity to task ordering, analyzing how different task sequences influence performance retention and forgetting; and (3) the evolution of low-rank adaptation parameters as new tasks are learned, revealing patterns of weight drift under different CL methods. Our findings suggest that existing CL methods struggle to maintain stability-plasticity balance when tasks with different objectives and supervision formats are interleaved. Code and full experimental setup will be publicly available.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2607.12048 [cs.CV]
(or arXiv:2607.12048v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.12048
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mai A. Shaaban [view email] [v1] Mon, 13 Jul 2026 18:08:26 UTC (3,854 KB)
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