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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.10912 |
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| _version_ | 1866915933913612288 |
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| author | Gao, Qiang Wang, Yi Zhang, Yong Li, Yong Deng, Yongbing Du, Lan Chen, Cunjian |
| author_facet | Gao, Qiang Wang, Yi Zhang, Yong Li, Yong Deng, Yongbing Du, Lan Chen, Cunjian |
| contents | Medical image segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10912 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation Gao, Qiang Wang, Yi Zhang, Yong Li, Yong Deng, Yongbing Du, Lan Chen, Cunjian Computer Vision and Pattern Recognition Medical image segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg. |
| title | TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.10912 |