Ambiguity Consistency and Uncertainty Minimization for Semi-Supervised Medical Image Segmentation
Xiaolin Huang, Biqing Zeng, Jiahui Pan, Yujiang Yao, Zheng Zhou, Bingzhi Chen · 2024
Co-training and pseudo-supervision are two common strategies in semi-supervised medical image segmentation. However, co-training may lead to a ’resonance’ problem, and the effectiveness of generating pseudo-labels by setting thresholds may greatly depend on manual efforts. To address these issues, we propose an innovative framework for Ambiguity Consistency and Uncertainty Minimization (ACUM) in semi-supervised medical image segmentation. Specifically, ACUM comprises two main components: (1) Ambiguity Consistency Constraint (ACC), which encourages model differentiation and applies dynamic pixel-level consistency constraints through ambiguous areas between sub-networks; (2) Pixel Uncertainty Minimization (PUM), which generates high-confidence pseudo-labels by selecting labels with relatively low uncertainty based on the uncertainty maps of sub-networks. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority of our proposed ACUM approach over state-of-the-art techniques.