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

2026-07-13 · Source: Computer Vision and Pattern Recognition · Field: Science & Research — Health & Medical Research, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

This work presents a systematic evaluation of continual learning (CL) for Medical Visual Question Answering (MedVQA) across diverse and heterogeneous clinical objectives. Addressing the challenge of catastrophic forgetting in real-world clinical deployments, the analysis explores CL methods' ability to retain knowledge when adapting to new tasks. Researchers investigated their sensitivity to task ordering and the evolution of low-rank adaptation parameters, revealing patterns of weight drift. The study covers tasks like classification, multi-label classification, detection, cell counting, and report generation. Findings indicate that current CL methods struggle to maintain a stability-plasticity balance when interleaving tasks with varied objectives and supervision formats. Code and the full experimental setup will be publicly available.

Key takeaway

For AI Scientists developing MedVQA systems, this analysis highlights critical limitations of current continual learning approaches when dealing with heterogeneous clinical tasks. You should prioritize research into novel CL methods that can better manage the stability-plasticity trade-off across diverse objectives and supervision formats. Carefully evaluate task ordering in your CL pipelines to mitigate catastrophic forgetting and ensure robust model adaptation in real-world clinical deployments.

Key insights

Existing continual learning methods struggle with heterogeneous medical VQA tasks due to stability-plasticity imbalance.

Principles

• CL methods face stability-plasticity challenges.
• Task ordering influences performance retention.
• Weight drift occurs under CL methods.

Method

The work systematically evaluates CL for MedVQA across diverse clinical objectives including classification, detection, and report generation, analyzing forgetting, task ordering sensitivity, and low-rank adaptation parameter evolution.

In practice

• Evaluate CL methods for MedVQA.
• Consider task sequencing impacts.
• Monitor low-rank adaptation parameters.

Topics

• Continual Learning
• Medical VQA
• Catastrophic Forgetting
• Heterogeneous Tasks
• Low-Rank Adaptation
• Clinical AI

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.