Med-SORA: Symptom to Organ Reasoning in Abdomen CT Images
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909895541915648 |
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| author | Na, You-Kyoung Cho, Yeong-Jun |
| author_facet | Na, You-Kyoung Cho, Yeong-Jun |
| contents | Understanding symptom-image associations is crucial for clinical reasoning. However, existing medical multimodal models often rely on simple one-to-one hard labeling, oversimplifying clinical reality where symptoms relate to multiple organs. In addition, they mainly use single-slice 2D features without incorporating 3D information, limiting their ability to capture full anatomical context. In this study, we propose Med-SORA, a framework for symptom-to-organ reasoning in abdominal CT images. Med-SORA introduces RAG-based dataset construction, soft labeling with learnable organ anchors to capture one-to-many symptom-organ relationships, and a 2D-3D cross-attention architecture to fuse local and global image features. To our knowledge, this is the first work to address symptom-to-organ reasoning in medical multimodal learning. Experimental results show that Med-SORA outperforms existing medical multimodal models and enables accurate 3D clinical reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Med-SORA: Symptom to Organ Reasoning in Abdomen CT Images Na, You-Kyoung Cho, Yeong-Jun Computer Vision and Pattern Recognition Understanding symptom-image associations is crucial for clinical reasoning. However, existing medical multimodal models often rely on simple one-to-one hard labeling, oversimplifying clinical reality where symptoms relate to multiple organs. In addition, they mainly use single-slice 2D features without incorporating 3D information, limiting their ability to capture full anatomical context. In this study, we propose Med-SORA, a framework for symptom-to-organ reasoning in abdominal CT images. Med-SORA introduces RAG-based dataset construction, soft labeling with learnable organ anchors to capture one-to-many symptom-organ relationships, and a 2D-3D cross-attention architecture to fuse local and global image features. To our knowledge, this is the first work to address symptom-to-organ reasoning in medical multimodal learning. Experimental results show that Med-SORA outperforms existing medical multimodal models and enables accurate 3D clinical reasoning. |
| title | Med-SORA: Symptom to Organ Reasoning in Abdomen CT Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.06752 |