Competitive dual-students using bi-level contrastive learning for semi-supervised medical image segmentation

作     者：Hu, Gang Zhao, Feng Houssein, Essam H. 

作者机构：Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt 

出 版 物：《ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE》 (Eng Appl Artif Intell)

年 卷 期：2025年第144卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China 

主　　题：Neural network Semi-supervised image segmentation Competitive dual-student Bi-level contrastive learning Class prototypes 

摘      要：Semi-supervised image segmentation aims to train the neural network with a small number of labeled images and a large number of unlabeled images, which helps to alleviate the burden of having less manually labeled medical data. However, the Mean-Teacher (MT) model, a benchmark method for semi-supervised medical segmentation, leads to a performance bottleneck as its student model eventually converges to the teacher model. In addition, existing segmentation methods treat all pixels equally and underestimate the importance of indistinguishable and underrepresented pixels, failing to mine the potential information in these regions effectively. To address the above issues, this paper proposes a Competitive Dual-Student (CDS) incorporating bi-level contrastive learning. First, an additional competitive dual-student model is added to the MT model and promoting knowledge sharing and complementarity among networks. Competitive instruction by the teacher through feature information exchange and positive comparisons reduces the accumulation of biased knowledge in the model. It stimulates the potential for further optimization of the model as a whole. Furthermore, a bi-level contrastive learning is designed. The high-level contrastive learning encourages competitive dual students to learn high-quality features from each other by constructing reliability constraints. The low-level contrastive achieved deep mining and accurate processing of local edge features by introducing class prototypes of high-quality features for teacher networks. Finally, the comprehensive experimental results on left atrium, brain tumor segmentation 2019 and automated cardiac diagnosis challenge datasets indicate that the segmentation performance of the proposed CDS outperforms the state-of-the-art compared methods. Code is released at https://***/FengZhao2001/CDS.