Semi-supervised learning has become increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional information. However, most existing semi-supervised segmentation methods only focus on extracting information from unlabeled data, disregarding the potential of labeled data to further improve the performance of the model. In this paper, we propose a novel Correlation Aware Mutual Learning (CAML) framework that leverages labeled data to guide the extraction of information from unlabeled data. Our approach is based on a mutual learning strategy that incorporates two modules: the Cross-sample Mutual Attention Module (CMA) and the Omni-Correlation Consistency Module (OCC). The CMA module establishes dense cross-sample correlations among a group of samples, enabling the transfer of label prior knowledge to unlabeled data. The OCC module constructs omni-correlations between the unlabeled and labeled datasets and regularizes dual models by constraining the omni-correlation matrix of each sub-model to be consistent. Experiments on the Atrial Segmentation Challenge dataset demonstrate that our proposed approach outperforms state-of-the-art methods, highlighting the effectiveness of our framework in medical image segmentation tasks. The codes, pre-trained weights, and data are publicly available.

翻译：半监督学习因其能利用大量未标记数据提取额外信息，在医学图像分割中日益流行。然而，现有大多数半监督分割方法仅关注从未标记数据中提取信息，忽视了标记数据进一步提升模型性能的潜力。本文提出了一种新颖的相关性感知相互学习（CAML）框架，利用标记数据指导从未标记数据中提取信息。该方法基于相互学习策略，包含两个模块：跨样本相互注意力模块（CMA）和全相关一致性模块（OCC）。CMA模块在一组样本间建立密集的跨样本相关性，实现将标签先验知识迁移至未标记数据。OCC模块构建未标记与标记数据集之间的全局相关性，并通过约束每个子模型的全相关矩阵保持一致性来规范双模型。在心房分割挑战数据集上的实验表明，所提方法优于现有最先进技术，验证了该框架在医学图像分割任务中的有效性。代码、预训练权重及数据均已公开。