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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.11666 |
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| _version_ | 1866911381934047232 |
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| author | Imran, Muhammad Lee, Chi Lee, Yugyung |
| author_facet | Imran, Muhammad Lee, Chi Lee, Yugyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11666 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MATEX: Multi-scale Attention and Text-guided Explainability of Medical Vision-Language Models Imran, Muhammad Lee, Chi Lee, Yugyung Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 68U10, 92C55 I.2.10; I.4.8; H.2.8; J.3 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. |
| title | MATEX: Multi-scale Attention and Text-guided Explainability of Medical Vision-Language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 68U10, 92C55 I.2.10; I.4.8; H.2.8; J.3 |
| url | https://arxiv.org/abs/2601.11666 |