In weakly supervised medical image segmentation, the absence of structural
priors and the discreteness of class feature distribution present a challenge,
i.e., how to accurately propagate supervision signals from local to global
regions without excessively spreading them to other irrelevant regions? To
address this, we propose a novel weakly supervised medical image segmentation
framework named PCLMix, comprising dynamic mix augmentation, pixel-level
contrastive learning, and consistency regularization strategies. Specifically,
PCLMix is built upon a heterogeneous dual-decoder backbone, addressing the
absence of structural priors through a strategy of dynamic mix augmentation
during training. To handle the discrete distribution of class features, PCLMix
incorporates pixel-level contrastive learning based on prediction uncertainty,
effectively enhancing the model's ability to differentiate inter-class pixel
differences and intra-class consistency. Furthermore, to reinforce segmentation
consistency and robustness, PCLMix employs an auxiliary decoder for dual
consistency regularization. In the inference phase, the auxiliary decoder will
be dropped and no computation complexity is increased. Extensive experiments on
the ACDC dataset demonstrate that PCLMix appropriately propagates local
supervision signals to the global scale, further narrowing the gap between
weakly supervised and fully supervised segmentation methods. Our code is
available at this https URL

提出了一种名为 PCLMix 的新型弱监督医学图像分割框架，通过引入动态混合增强、像素级对比学习和一致性正则化策略，解决了弱监督图像分割中局部到全局区域的准确传播监督信号的挑战，并成功缩小了弱监督和全监督分割方法之间的差距。

PCLMix: 弱监督医学图像分割的像素级对比学习和动态混合增强

PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level  Contrastive Learning and Dynamic Mix Augmentation

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。