mlsanityData to 7 Oct 2026

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

arXiv 2510.11287 ↗First posted 13 Oct 2025cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 13 Oct 2025.

Builds on

19 of 27 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  4. 2017Deformable Convolutional Networkscited by 1,481
  5. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  6. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  7. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  8. 2017Gated Multimodal Units for Information Fusioncited by 244
  9. 2023SAM-Med2Dcited by 123
  10. 2023Explicit Visual Prompting for Low-Level Structure Segmentationscited by 113
  11. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  12. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
7 more
  1. 2023EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentationcited by 53
  2. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentationcited by 45
  3. 2023CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Queryingcited by 34
  4. 2023Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentationcited by 29
  5. 2024Depth-aware Test-Time Training for Zero-shot Video Object Segmentationcited by 27
  6. 2024Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attentioncited by 26
  7. 2023Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurringcited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptationcited by 0
  2. 2026DCSNet: Multiscale Feature Aggregation for Small Medical Object Segmentation with Detection-guided Hierarchical Croppingcited by 0