Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning

2025-05-24

AI Summary

Medical large vision-language models (Med-LVLMs) frequently suffer from hallucinations and erroneous outputs due to misaligned visual attention distributions. To address this, we propose A³Tune, an automated attention alignment fine-tuning framework. Our method introduces two key innovations: (1) a zero-shot, weakly supervised attention head recalibration mechanism that identifies and corrects biased attention without human annotations; and (2) the A³MoE module, which integrates SAM-based zero-shot segmentation and BioMedCLIP-guided semantic prompting to enable adaptive expert routing and parameter scheduling across multimodal prompts. Evaluated on medical visual question answering and radiology report generation, A³Tune achieves significant improvements over state-of-the-art methods. Crucially, its learned attention distributions better conform to clinical anatomical priors, yielding simultaneous gains in both quantitative accuracy and clinical plausibility.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Medical Large Vision-Language Models (Med-LVLMs) often exhibit suboptimal attention distribution on visual inputs, leading to hallucinated or inaccurate outputs. Existing mitigation methods primarily rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. To address this, we propose A$^3$Tune, a novel fine-tuning framework for Automatic Attention Alignment Tuning. A$^3$Tune leverages zero-shot weak labels from SAM, refines them into prompt-aware labels using BioMedCLIP, and then selectively modifies visually-critical attention heads to improve alignment while minimizing interference. Additionally, we introduce a A$^3$MoE module, enabling adaptive parameter selection for attention tuning across diverse prompts and images. Extensive experiments on medical VQA and report generation benchmarks show that A$^3$Tune outperforms state-of-the-art baselines, achieving enhanced attention distributions and performance in Med-LVLMs.

Problem

• Improving attention distribution in medical vision-language models
• Reducing hallucinated or inaccurate outputs in Med-LVLMs
• Enhancing alignment without additional supervision

Innovation

• Automatic Attention Alignment Tuning framework
• Zero-shot weak labels from SAM
• Adaptive parameter selection via A$^3$MoE