Abstract
Since the task of annotating medical image labels is pixel-level and needs to be depicted by trained experts, there are few large-scale medical image datasets with annotations. Semi-Supervised Learning (SSL) has become the focus of research for medical image segmentation tasks. The key techniques for our Segmentation method are Mixup and Mutual Information (MMISeg), which involve consistency-based regularization and unsupervised representation learning. On the one hand, we utilize an interpolation-based method to mix unlabeled data, and minimize consistency regularization. On the other hand, by taking the feature of the encoder stage as global feature and the feature of the decoder stage as local feature, we maximize mutual information of global and local features which are from two different transformations of the same image, respectively. Experimental results show that MMISeg outperforms existing semi-supervised methods.
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Huang, Y., Pan, H., Zeng, Z. (2022). MMISeg: A Semi-supervised Segmentation Method Based on Mixup and Mutual Information for Cardiac MRI Segmentation. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_17
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