{"type":"ScholarlyArticle","title":"Meta Segmentation Network for Ultra-Resolution Medical Images","authors":[{"name":"Tong Wu","slug":"tong-wu","given":"Tong","family":"Wu","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , School of Informatics , Xiamen University , Fujian , China"},{"name":"Bicheng Dai","slug":"bicheng-dai","given":"Bicheng","family":"Dai","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , School of Informatics , Xiamen University , Fujian , China"},{"name":"Shuxin Chen","slug":"shuxin-chen","given":"Shuxin","family":"Chen","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , School of Informatics , Xiamen University , Fujian , China"},{"name":"Yanyun Qu","slug":"yanyun-qu","given":"Yanyun","family":"Qu","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , School of Informatics , Xiamen University , Fujian , China"},{"name":"Yuan Xie","slug":"yuan-xie","given":"Yuan","family":"Xie","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai , China"}],"abstract":"Despite recent great progress on semantic segmentation, there still exist huge challenges in medical ultra-resolution image segmentation. The methods based on multi-branch structure can make a good balance between computational burdens and segmentation accuracy. However, the fusion structure in these methods require to be designed elaborately to achieve desirable result, which leads to model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to solve this challenging problem. With the help of meta-learning, the fusion module of MSN is quite simple but effective. MSN can fast generate the weights of fusion layers through a simple meta-learner, requiring only a few training samples and epochs to converge. In addition, to avoid learning all branches from scratch, we further introduce a particular weight sharing mechanism to realize a fast knowledge adaptation and share the weights among multiple branches, resulting in the performance improvement and significant parameters reduction. The experimental results on two challenging ultra-resolution medical datasets BACH and ISIC show that MSN achieves the best performance compared with the state-of-the-art approaches.","doi":"10.24963/ijcai.2020/76","url":"https://ijcai.org/proceedings/2020/76","pdf":"https://ijcai.org/proceedings/2020/0076.pdf","markdown":"https://ijcai.org/proceedings/2020/76.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/76","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":76,"pages":{"first":544,"last":550},"keywords":["Computer Vision: Biomedical Image Understanding","Computer Vision: 2D and 3D Computer Vision"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24322/file_24322.m3u8","length_sec":290,"provider":"hls"}],"references":[{"position":1,"raw":"[Altunbay et al., 2009] Dogan Altunbay, Celal Cigir, Cenk Sokmensuer, and Cigdem Gunduz-Demir. 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