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.
https://github.com/Herschel555/CAML
S. Gao and Z. Zhang—Both authors contributed equally to this work.
Z. Zhang—Work done as an intern in Deepwise AI Lab.
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Acknowledgements
This work is funded by the Scientific and Technological Innovation 2030 New Generation Artificial Intelligence Project of the National Key Research and Development Program of China (No. 2021ZD0113302), Beijing Municipal Science and Technology Planning Project (No. Z201100005620008, Z211100003521009).
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Gao, S., Zhang, Z., Ma, J., Li, Z., Zhang, S. (2023). Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_10
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