SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915508159250432 |
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| author | Hu, Yingzhen Zhong, Yiheng Li, Ruobing Su, Yingxue An, Jiabao Tang, Feilong Su, Jionglong Razzak, Imran |
| author_facet | Hu, Yingzhen Zhong, Yiheng Li, Ruobing Su, Yingxue An, Jiabao Tang, Feilong Su, Jionglong Razzak, Imran |
| contents | The Segment Anything Model (SAM) demonstrates impressive zero-shot segmentation ability on natural images but encounters difficulties in medical imaging due to domain shifts, anatomical variability, and its reliance on user-provided prompts. Recent prompt-free adaptations alleviate the need for expert intervention, yet still suffer from limited robustness and adaptability, often overlooking the issues of semantic over-smoothing and token uniformity. We propose SAM-DCE, which balances local discrimination and global semantics while mitigating token uniformity, enhancing inter-class separability, and enriching mask decoding with fine-grained, consistent representations. Extensive experiments on diverse medical benchmarks validate its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16886 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation Hu, Yingzhen Zhong, Yiheng Li, Ruobing Su, Yingxue An, Jiabao Tang, Feilong Su, Jionglong Razzak, Imran Computer Vision and Pattern Recognition The Segment Anything Model (SAM) demonstrates impressive zero-shot segmentation ability on natural images but encounters difficulties in medical imaging due to domain shifts, anatomical variability, and its reliance on user-provided prompts. Recent prompt-free adaptations alleviate the need for expert intervention, yet still suffer from limited robustness and adaptability, often overlooking the issues of semantic over-smoothing and token uniformity. We propose SAM-DCE, which balances local discrimination and global semantics while mitigating token uniformity, enhancing inter-class separability, and enriching mask decoding with fine-grained, consistent representations. Extensive experiments on diverse medical benchmarks validate its effectiveness. |
| title | SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.16886 |