Hengyang Liu1, Yang Yuan1,*, Pengcheng Ren1, Chengyun Song1, Fen Luo2
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 543-560, 2025, DOI:10.32604/cmc.2024.056478
- 03 January 2025
Abstract Existing semi-supervised medical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch. However, current copy-paste methods have three limitations: (1) training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information; (2) low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data; (3) the segmentation performance in low-contrast and local regions is less than optimal. We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy (SADT), which enhances feature diversity and learns high-quality features to overcome these problems. To be more… More >
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