{"type":"ScholarlyArticle","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","authors":[{"name":"Zhiqiang Shen","slug":"zhiqiang-shen","given":"Zhiqiang","family":"Shen","affiliation":"College of Computer Science and Engineering , Northeastern University , Shenyang , China; Key Laboratory of Intelligent Computing in Medical Image , Ministry of Education , Shenyang , China"},{"name":"Peng Cao","slug":"peng-cao","given":"Peng","family":"Cao","affiliation":"College of Computer Science and Engineering , Northeastern University , Shenyang , China; Key Laboratory of Intelligent Computing in Medical Image , Ministry of Education , Shenyang , China"},{"name":"Hua Yang","slug":"hua-yang","given":"Hua","family":"Yang","affiliation":"College of Photonic and Electronic Engineering , Fujian Normal University , Fuzhou , China"},{"name":"Xiaoli Liu","slug":"xiaoli-liu","given":"Xiaoli","family":"Liu","affiliation":"DAMO Academy , Alibaba Group , China"},{"name":"Jinzhu Yang","slug":"jinzhu-yang","given":"Jinzhu","family":"Yang","affiliation":"College of Computer Science and Engineering , Northeastern University , Shenyang , China; Key Laboratory of Intelligent Computing in Medical Image , Ministry of Education , Shenyang , China"},{"name":"Osmar R. Zaiane","slug":"osmar-r-zaiane","given":"Osmar R.","family":"Zaiane","affiliation":"Alberta Machine Intelligence Institute , University of Alberta , Edmonton , Alberta , Canada"}],"abstract":"Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. \r\n    To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels. \r\n    Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.\r\n    Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art. \r\n    Code is available at: https://github.com/Senyh/UCMT.","doi":"10.24963/ijcai.2023/467","url":"https://ijcai.org/proceedings/2023/467","pdf":"https://ijcai.org/proceedings/2023/0467.pdf","markdown":"https://ijcai.org/proceedings/2023/467.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/467","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":467,"pages":{"first":4199,"last":4207},"keywords":["Machine Learning: ML: Semi-supervised learning","Computer Vision: CV: Segmentation","Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[{"position":1,"raw":"[Bernal et al., 2015] Jorge Bernal, F Javier Sánchez, Glo- ria Fernández-Esparrach, Debora Gil, Cristina Rodríguez, and Fernando Vilariño. 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