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{this https URL}{GitHub}

皮肤病变分割在皮肤病的早期检测和准确诊断中起着至关重要的作用。本论文提出了一种新的框架 DermoSegDiff，它在学习过程中融入边界信息，并引入了一种新的损失函数来优先考虑边界，并逐渐降低其他区域的重要性。我们还引入了一种基于 U-Net 的去噪网络，在网络内部高效地集成了噪声和语义信息。在多个皮肤分割数据集上的实验结果表明，DermoSegDiff 相比现有的 CNN、Transformer 和扩散方法在各种情况下均具有优越性，展示了其效果和泛化能力。该实现可在 GitHub 上公开访问。