Papers › DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Afshin Bozorgpour, Yousef Sadegheih, Amirhossein Kazerouni, Reza Azad, Dorit Merhof
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently gained attention for their exceptional image-generation capabilities. Building on these advancements, we propose DermoSegDiff, a novel framework for skin lesion segmentation that incorporates boundary information during the learning process. Our approach introduces a novel loss function that prioritizes the boundaries during training, gradually reducing the significance of other regions. We also introduce a novel U-Net-based denoising network that proficiently integrates noise and semantic information inside the network. Experimental results on multiple skin segmentation datasets demonstrate the superiority of DermoSegDiff over existing CNN, transformer, and diffusion-based approaches, showcasing its effectiveness and generalization in various scenarios. The implementation is publicly accessible on \href{https://github.com/mindflow-institue/dermosegdiff}{GitHub}
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Lesion Segmentation | HAM10000 | DermoSegDiff-B | Dice Score | 0.943 | #1 of 1 | Archive leaderboard | report |
| Lesion Segmentation | ISIC 2018 | DermoSegDiff-A | mean Dice | 0.9005 | #8 of 17 | Archive leaderboard | report |
| Lesion Segmentation | PH2 | DermoSegDiff-B | Dice Score | 0.9467 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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