V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912452778655744 |
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| author | Wang, Yuan Liu, Jiaxiang Gao, Shujian Feng, Bin Tang, Zhihang Gai, Xiaotang Wu, Jian Liu, Zuozhu |
| author_facet | Wang, Yuan Liu, Jiaxiang Gao, Shujian Feng, Bin Tang, Zhihang Gai, Xiaotang Wu, Jian Liu, Zuozhu |
| contents | Recent advances in multimodal techniques have led to significant progress in Medical Visual Question Answering (Med-VQA). However, most existing models focus on global image features rather than localizing disease-specific regions crucial for diagnosis. Additionally, current research tends to emphasize answer accuracy at the expense of the reasoning pathway, yet both are crucial for clinical decision-making. To address these challenges, we propose From Vision to Text Chain-of-Thought (V2T-CoT), a novel approach that automates the localization of preference areas within biomedical images and incorporates this localization into region-level pixel attention as knowledge for Vision CoT. By fine-tuning the vision language model on constructed R-Med 39K dataset, V2T-CoT provides definitive medical reasoning paths. V2T-CoT integrates visual grounding with textual rationale generation to establish precise and explainable diagnostic results. Experimental results across four Med-VQA benchmarks demonstrate state-of-the-art performance, achieving substantial improvements in both performance and interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19610 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis Wang, Yuan Liu, Jiaxiang Gao, Shujian Feng, Bin Tang, Zhihang Gai, Xiaotang Wu, Jian Liu, Zuozhu Computational Engineering, Finance, and Science Recent advances in multimodal techniques have led to significant progress in Medical Visual Question Answering (Med-VQA). However, most existing models focus on global image features rather than localizing disease-specific regions crucial for diagnosis. Additionally, current research tends to emphasize answer accuracy at the expense of the reasoning pathway, yet both are crucial for clinical decision-making. To address these challenges, we propose From Vision to Text Chain-of-Thought (V2T-CoT), a novel approach that automates the localization of preference areas within biomedical images and incorporates this localization into region-level pixel attention as knowledge for Vision CoT. By fine-tuning the vision language model on constructed R-Med 39K dataset, V2T-CoT provides definitive medical reasoning paths. V2T-CoT integrates visual grounding with textual rationale generation to establish precise and explainable diagnostic results. Experimental results across four Med-VQA benchmarks demonstrate state-of-the-art performance, achieving substantial improvements in both performance and interpretability. |
| title | V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2506.19610 |