MATEX: Multi-scale Attention and Text-guided Explainability of Medical Vision-Language Models

2026-01-16

AI Summary

This work addresses the limited interpretability of existing medical vision-language models, which stems from their lack of anatomical grounding, imprecise spatial localization, and coarse attention granularity. To overcome these limitations, the authors propose a novel approach that integrates multi-scale attention rollout, text-guided spatial priors, and inter-layer consistency analysis to generate anatomically aligned, high-precision, and stable gradient attribution maps. Experimental results on the MS-CXR dataset demonstrate that the proposed method outperforms the current state-of-the-art M2IB model in both spatial localization accuracy and alignment with expert-annotated lesion regions, thereby significantly enhancing the clinical interpretability of medical AI systems.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce MATEX (Multi-scale Attention and Text-guided Explainability), a novel framework that advances interpretability in medical vision-language models by incorporating anatomically informed spatial reasoning. MATEX synergistically combines multi-layer attention rollout, text-guided spatial priors, and layer consistency analysis to produce precise, stable, and clinically meaningful gradient attribution maps. By addressing key limitations of prior methods, such as spatial imprecision, lack of anatomical grounding, and limited attention granularity, MATEX enables more faithful and interpretable model explanations. Evaluated on the MS-CXR dataset, MATEX outperforms the state-of-the-art M2IB approach in both spatial precision and alignment with expert-annotated findings. These results highlight MATEX's potential to enhance trust and transparency in radiological AI applications.

Problem

• interpretability
• medical vision-language models
• spatial precision
• anatomical grounding
• attention granularity

Innovation

• Multi-scale Attention
• Text-guided Explainability
• Medical Vision-Language Models
• Anatomically Informed Reasoning
• Gradient Attribution