Weakly supervised medical image segmentation (MIS) using generative models is
crucial for clinical diagnosis. However, the accuracy of the segmentation
results is often limited by insufficient supervision and the complex nature of
medical imaging. Existing models also only provide a single outcome, which does
not allow for the measurement of uncertainty. In this paper, we introduce
DiffSeg, a segmentation model for skin lesions based on diffusion difference
which exploits diffusion model principles to ex-tract noise-based features from
images with diverse semantic information. By discerning difference between
these noise features, the model identifies diseased areas. Moreover, its
multi-output capability mimics doctors' annotation behavior, facilitating the
visualization of segmentation result consistency and ambiguity. Additionally,
it quantifies output uncertainty using Generalized Energy Distance (GED),
aiding interpretability and decision-making for physicians. Finally, the model
integrates outputs through the Dense Conditional Random Field (DenseCRF)
algorithm to refine the segmentation boundaries by considering inter-pixel
correlations, which improves the accuracy and optimizes the segmentation
results. We demonstrate the effectiveness of DiffSeg on the ISIC 2018 Challenge
dataset, outperforming state-of-the-art U-Net-based methods.

DiffSeg 基于扩散差异原理，利用扩散模型从具有多样的语义信息的图像中提取基于噪声的特征，识别出疾病区域，同时通过模拟医生的注释行为，可视化分割结果的一致性和模糊性，并使用广义能量距离（GED）量化输出不确定性，帮助医生解释和决策。最后，通过密集条件随机场（DenseCRF）算法整合输出，考虑像素之间的相关性，改善准确度并优化分割结果。我们在 ISIC 2018 Challenge 数据集中展示了 DiffSeg 的有效性，优于现有基于 U-Net 的方法。