  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.
