Diff-CL：一种用于半监督医学图像分割的新型交叉伪监督方法
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation
AI总结：
本文提出半监督医学图像分割框架Diff-CL，通过扩散模型与卷积网络的交叉伪监督、高频mamba模块和对比学习标签传播，在左心房、脑肿瘤和NIH胰腺三个数据集上取得SOTA性能。
摘要：
半监督学习利用无标签数据中的信息来提升模型泛化能力，从而减少对大规模有标签数据集的依赖。现有研究大多聚焦于有限样本，未能捕捉整体数据分布。我们认为，将分布信息与细节信息相结合对于获得更稳健、更准确的分割结果至关重要。一方面，扩散模型（DM）凭借其强大的生成能力，能够有效学习数据分布；然而，它在精细细节捕捉方面存在困难，导致生成的图像带有误导性细节。将扩散模型与卷积神经网络（CNN）结合，可以使前者学习数据分布，而后者修正精细细节。但通过CNN捕捉完整的高频细节需要大量计算资源，并且容易受到局部噪声的影响。另一方面，鉴于有标签数据和无标签数据来自同一分布，我们认为无标签数据中与有标签数据整体类别语义相似的区域很可能属于同一类别，而相似度极低的区域则不太可能属于同一类别。本文从分布视角提出了一种半监督医学图像分割框架（Diff-CL）。首先，我们提出了扩散分割网络与卷积分割网络之间的交叉伪监督学习机制。其次，我们设计了一个高频mamba模块，以全局方式捕捉边界和细节信息。最后，我们应用对比学习实现从有标签数据到无标签数据的标签传播。我们的方法在三个数据集上取得了最先进（SOTA）的性能，包括左心房、脑肿瘤和NIH胰腺数据集。
英文摘要：
Semi-supervised learning utilizes insights from unlabeled data to improve model generalization, thereby reducing reliance on large labeled datasets. Most existing studies focus on limited samples and fail to capture the overall data distribution. We contend that combining distributional information with detailed information is crucial for achieving more robust and accurate segmentation results. On the one hand, with its robust generative capabilities, diffusion models (DM) learn data distribution effectively. However, it struggles with fine detail capture, leading to generated images with misleading details. Combining DM with convolutional neural networks (CNNs) enables the former to learn data distribution while the latter corrects fine details. While capturing complete high-frequency details by CNNs requires substantial computational resources and is susceptible to local noise. On the other hand, given that both labeled and unlabeled data come from the same distribution, we believe that regions in unlabeled data similar to overall class semantics to labeled data are likely to belong to the same class, while regions with minimal similarity are less likely to. This work introduces a semi-supervised medical image segmentation framework from the distribution perspective (Diff-CL). Firstly, we propose a cross-pseudo-supervision learning mechanism between diffusion and convolution segmentation networks. Secondly, we design a high-frequency mamba module to capture boundary and detail information globally. Finally, we apply contrastive learning for label propagation from labeled to unlabeled data. Our method achieves state-of-the-art (SOTA) performance across three datasets, including left atrium, brain tumor, and NIH pancreas datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
