EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism

Han Xia, Quanjun Li, Qian Li, Zimeng Li, Hongbin Ye +3 more
10/13/2025
cs.CV

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Abstract

Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.

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Cite this paper

@article{xia2025eems,
  title  = {EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism},
  author = {Han Xia and Quanjun Li and Qian Li and Zimeng Li and Hongbin Ye and Yupeng Liu and Haolun Li and Xuhang Chen},
  year   = {2025},
  eprint = {2510.11287},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.11287v1}
}

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