RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866911687699857408 |
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| author | Yang, Jiayan Wu, Zhuoyu Fang, Wenqi |
| author_facet | Yang, Jiayan Wu, Zhuoyu Fang, Wenqi |
| contents | Vision-Language Models (VLMs) facilitate medical visual question answering (MedVQA) by jointly interpreting images and text. However, existing models typically depend on large architectures and closed-set answers, which limits their efficiency and potential clinical applicability. To overcome these shortcomings, we introduce RoiMAM, an efficient VLM. It integrates a training-free ROI Generation Module with Semantic Selective Suppression to focus on lesion-relevant regions, alongside a Text Prompt Enhancer module that provides modality-specific context without introducing training parameters. Compared to the widely used MedVInT-TD model, our design achieves efficient and accurate diagnosis at less than 20\% of the model size, while improving accuracy by approximately 2% on SLAKE and 4.6% on PMC-VQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15561 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding Yang, Jiayan Wu, Zhuoyu Fang, Wenqi Computer Vision and Pattern Recognition Vision-Language Models (VLMs) facilitate medical visual question answering (MedVQA) by jointly interpreting images and text. However, existing models typically depend on large architectures and closed-set answers, which limits their efficiency and potential clinical applicability. To overcome these shortcomings, we introduce RoiMAM, an efficient VLM. It integrates a training-free ROI Generation Module with Semantic Selective Suppression to focus on lesion-relevant regions, alongside a Text Prompt Enhancer module that provides modality-specific context without introducing training parameters. Compared to the widely used MedVInT-TD model, our design achieves efficient and accurate diagnosis at less than 20\% of the model size, while improving accuracy by approximately 2% on SLAKE and 4.6% on PMC-VQA. |
| title | RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.15561 |