{"type":"ScholarlyArticle","title":"Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift","authors":[{"name":"Qinming Zhang","slug":"qinming-zhang","given":"Qinming","family":"Zhang","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Luyan Liu","slug":"luyan-liu","given":"Luyan","family":"Liu","affiliation":"Tencent Jarvis Lab , Shenzhen , China"},{"name":"Kai Ma","slug":"kai-ma","given":"Kai","family":"Ma","affiliation":"Tencent Jarvis Lab , Shenzhen , China"},{"name":"Cheng Zhuo","slug":"cheng-zhuo","given":"Cheng","family":"Zhuo","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Yefeng Zheng","slug":"yefeng-zheng","given":"Yefeng","family":"Zheng","affiliation":"Tencent Jarvis Lab , Shenzhen , China"}],"abstract":"Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, domain shift and corrupted annotations, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selected by the peer network to combat with corrupted labels. In addition, a noise-tolerant loss is proposed to encourage the network to capture the key location and filter the discrepancy under various noise-contaminant labels. To further reduce the accumulated error, we introduce a class-imbalanced cross learning using most confident predictions at class-level. Experimental results on REFUGE and Drishti-GS datasets for optic disc (OD) and optic cup (OC) segmentation demonstrate the superior performance of our proposed approach to the state-of-the-art methods.","doi":"10.24963/ijcai.2020/146","url":"https://ijcai.org/proceedings/2020/146","pdf":"https://ijcai.org/proceedings/2020/0146.pdf","markdown":"https://ijcai.org/proceedings/2020/146.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/146","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Computer Vision","track":"Main track","number":146,"pages":{"first":1047,"last":1053},"keywords":["Computer Vision: 2D and 3D Computer Vision","Machine Learning Applications: Bio/Medicine","Machine Learning Applications: Applications of Unsupervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24095/file_24095.m3u8","length_sec":289,"provider":"hls"}],"references":[{"position":1,"raw":"[Chang et al., 2019] Wei-Lun Chang, Hui-Po Wang, Wen- Hsiao Peng, and Wei-Chen Chiu. 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