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.

翻译：利用生成模型进行弱监督医学图像分割（MIS）对临床诊断至关重要。然而，分割结果的准确性常受限于监督不足和医学影像的复杂性。现有模型仅提供单一输出，无法量化不确定性。本文提出DiffSeg——一种基于扩散差异的皮肤病变分割模型，该模型利用扩散模型原理从具有多样语义信息的图像中提取基于噪声的特征。通过辨析这些噪声特征之间的差异，模型可识别病变区域。此外，其多输出能力模拟了医生的标注行为，有助于可视化分割结果的一致性与模糊性。同时，模型采用广义能量距离（GED）量化输出不确定性，提升可解释性并辅助医生决策。最终，模型通过密集条件随机场（DenseCRF）算法整合输出结果，考虑像素间相关性细化分割边界，从而提高精度并优化分割结果。在ISIC 2018挑战数据集上的实验表明，DiffSeg的性能优于当前最先进的基于U-Net的方法。