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}