{"type":"ScholarlyArticle","title":"Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation","authors":[{"name":"Tao Wang","slug":"tao-wang","given":"Tao","family":"Wang","affiliation":"College of Computer Science and Software Engineering , Shenzhen University , Shenzhen , China"},{"name":"Jianglin Lu","slug":"jianglin-lu","given":"Jianglin","family":"Lu","affiliation":"College of Computer Science and Software Engineering , Shenzhen University , Shenzhen , China"},{"name":"Zhihui Lai","slug":"zhihui-lai","given":"Zhihui","family":"Lai","affiliation":"College of Computer Science and Software Engineering , Shenzhen University , Shenzhen , China; Shenzhen Institute of Artificial Intelligence and Robotics for Society , Shenzhen , China"},{"name":"Jiajun Wen","slug":"jiajun-wen","given":"Jiajun","family":"Wen","affiliation":"College of Computer Science and Software Engineering , Shenzhen University , Shenzhen , China"},{"name":"Heng Kong","slug":"heng-kong","given":"Heng","family":"Kong","affiliation":"Shenzhen University General Hospital , Shenzhen , China"}],"abstract":"Recently, contrastive learning has shown great potential in medical image segmentation. Due to the lack of expert annotations, however, it is challenging to apply contrastive learning in semi-supervised scenes. To solve this problem, we propose a novel uncertainty-guided pixel contrastive learning method for semi-supervised medical image segmentation. Specifically, we construct an uncertainty map for each unlabeled image and then remove the uncertainty region in the uncertainty map to reduce the possibility of noise sampling. The uncertainty map is determined by a well-designed consistency learning mechanism, which generates comprehensive predictions for unlabeled data by encouraging consistent network outputs from two different decoders. In addition, we suggest that the effective global representations learned by an image encoder should be equivariant to different geometric transformations. To this end, we construct an equivariant contrastive loss to strengthen global representation learning ability of the encoder. Extensive experiments conducted on popular medical image benchmarks demonstrate that the proposed method achieves better segmentation performance than the state-of-the-art methods.","doi":"10.24963/ijcai.2022/201","url":"https://ijcai.org/proceedings/2022/201","pdf":"https://ijcai.org/proceedings/2022/0201.pdf","markdown":"https://ijcai.org/proceedings/2022/201.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/201","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":201,"pages":{"first":1444,"last":1450},"keywords":["Computer Vision: Biomedical Image Analysis","Computer Vision: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: Segmentation"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986816","length_sec":83,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984959","length_sec":356,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bernard et al., 2018] Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng- Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. 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