Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2307.06312v1
• Date: Wed, 12 Jul 2023 17:20:05 GMT
• Title: Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation
• Authors: Shengbo Gao, Ziji Zhang, Jiechao Ma, Zihao Li and Shu Zhang
• Abstract summary: Most existing semi-supervised segmentation methods only focus on extracting information from unlabeled data. We propose a novel Correlation Aware Mutual Learning 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)
• Score: 5.045813144375637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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