# An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering

Source: [An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering](<https://www.sota2.com/research/paper/6a5713ea32fb9373148784be>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-16T05:00:01.138Z

Authors: Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed, Dilnaz Utemissova, Ufaq Khan, Mohammad Yaqub

Published: 2026-07-13T18:08:26.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2607.12048>)

[PDF](<https://arxiv.org/pdf/2607.12048.pdf>)