Abstract
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.
Abstract (translated)
弱监督下的医学图像分割（MIS）利用生成模型在临床诊断中至关重要。然而，分割结果的准确性常常受到监督不足和医学图像复杂性的限制。现有的模型仅提供单一输出，无法衡量不确定性。在本文中，我们介绍了DiffSeg，一种基于扩散差分的皮肤病变分割模型，它利用扩散模型原理从具有丰富语义信息的图像中提取噪声基于特征。通过鉴别这些噪声特征，模型识别出病变区域。此外，其多输出能力模仿了医生的标注行为，有助于可视化分割结果的一致性和不确定性。此外，通过使用泛化能量距离（GED）量化输出不确定性，有助于医生更好地解释和做出决策。最后，通过Dense Conditional Random Field（DenseCRF）算法将输出集成，通过考虑像素间关联来平滑分割边界，从而提高准确性和优化分割结果。我们在ISIC 2018挑战数据集上证明了DiffSeg的有效性，超越了基于U-Net的最先进方法。
URL
https://arxiv.org/abs/2404.16474
