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

2026-07-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, long

Summary

This study empirically analyzes continual learning (CL) methods for heterogeneous Medical Visual Question Answering (MedVQA) systems, which must adapt to new clinical tasks without forgetting prior knowledge. Researchers systematically evaluated five CL strategies—Replay, Elastic Weight Consolidation (EWC), Knowledge Distillation (KD), Soft-masking of Parameter-level Gradient flow (SPG), and Orthogonal Low-Rank Adaptation (O-LoRA)—alongside multi-task learning (MTL) and sequential fine-tuning (SEQ-FT) baselines. Experiments used the MedGemma-4b-it model with LoRA (rank 16, alpha 32) on the FLARE-MLLM-2D benchmark, covering classification, multi-label classification, detection, cell counting, and report generation. Findings indicate that current CL methods struggle to maintain a stability-plasticity balance across diverse objectives and supervision formats. EWC demonstrated the most consistent performance, while task ordering, particularly the early introduction of report generation, significantly impacted forgetting and model behavior. LoRA parameter drift patterns correlated with observed forgetting.

Key takeaway

For Machine Learning Engineers deploying medical VQA systems in evolving clinical environments, you should recognize that current continual learning methods struggle with heterogeneous tasks. Prioritize Elastic Weight Consolidation (EWC) for its more consistent stability-plasticity balance. Carefully consider task sequencing, especially avoiding early introduction of report generation, as it significantly impacts model stability and forgetting. You may need to explore functional alignment beyond parameter-level constraints for robust adaptation.

Key insights

Existing continual learning methods struggle with stability-plasticity balance in heterogeneous medical VQA tasks.

Principles

• Heterogeneous tasks challenge CL stability-plasticity.
• Task ordering significantly impacts CL performance.
• LoRA parameter drift correlates with forgetting.

Method

The study systematically evaluated CL strategies by reformulating them to operate solely on LoRA parameters of a pre-trained medical VLM, training sequentially on diverse MedVQA tasks, and analyzing forgetting ratios and LoRA weight drift.

In practice

• Prioritize EWC for MedVQA continual learning.
• Carefully sequence tasks, avoid early report generation.
• Monitor LoRA parameter drift for stability.

Topics

• Continual Learning
• Medical VQA
• Low-Rank Adaptation
• Catastrophic Forgetting
• Vision-Language Models
• Task Heterogeneity

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering — Existing continual learning methods struggle with heterogeneous medical VQA tasks due to stability-plasticity imbalance.
• Revisiting Weight Regularization for Low-Rank Continual Learning — Weight regularization effectively mitigates task interference in low-rank continual learning with pre-trained models.
• Noise-Aware Visual Representation Learning for Medical Visual Question Answering — Learning robust visual representations through denoising enhances Medical Visual Question Answering performance and robustness.
• PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective — PEFT evaluation should balance task adaptation (plasticity) with pretrained knowledge retention (stability), where orthogonal finetuning excels.
• Can a Language Model Learn Facts Continually in Its Weights? — Continual learning in language model weights struggles with fact reachability, not storage, making context a more reliable channel.
• Citation-Aware Continual Pre-Training for Biomedical Language Models — Incorporating citation graph data into language model pre-training significantly improves biomedical QA accuracy.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.