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Semi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation.

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第一作者： Yang,Yang

第一单位： School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China.

作者： Yang,Yang ^[1] ; Guoying,Sun ^[1] ; Tong,Zhang ^[2] ; Ruixuan,Wang ^[3] ; Jingyong,Su ^[4]

作者单位： School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China. ^[1] Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen, 518055, China. ^[2] School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen, 518055, China. Electronic address: wangruix5@mail.sysu.edu.cn. ^[3] School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China; National Key Laboratory of Smart Farm Technologies and Systems, Harbin, 150001, China. Electronic address: sujingyong@hit.edu.cn. ^[4]

关键词 Contrastive learning Image segmentation Semi-supervised learning Uncertainty estimation

医学主题词人类(Humans);算法(Algorithms);图像处理, 计算机辅助(Image Processing, Computer-Assisted);图像解释, 计算机辅助(Image Interpretation, Computer-Assisted)

DOI 10.1016/j.media.2024.103450

PMID 39798528

发布时间 2025-04-30

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