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arXiv · 2509.16886

SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation

Yingzhen Hu·Yiheng Zhong·Ruobing Li·Yingxue Su·Jiabao An·Feilong Tang·Jionglong Su·Imran Razzak
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Abstract

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.

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Yingzhen Hu, Yiheng Zhong, Ruobing Li, Yingxue Su, Jiabao An, Feilong Tang, Jionglong Su, Imran Razzak. 2025-09-21. SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation. https://arxiv.org/abs/2509.16886

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