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Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training.
Lin, Yi; Qu, Zhiyong; Chen, Hao; Gao, Zhongke; Li, Yuexiang; Xia, Lili; Ma, Kai; Zheng, Yefeng; Cheng, Kwang-Ting.
Affiliation
  • Lin Y; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong.
  • Qu Z; School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
  • Chen H; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong. Electronic address: jhc@ces.ust.hk.
  • Gao Z; School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
  • Li Y; Tencent Jarvis Lab, Shenzhen, China.
  • Xia L; School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
  • Ma K; Tencent Jarvis Lab, Shenzhen, China.
  • Zheng Y; Tencent Jarvis Lab, Shenzhen, China.
  • Cheng KT; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong.
Med Image Anal ; 89: 102933, 2023 10.
Article in En | MEDLINE | ID: mdl-37611532
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Cell Nucleus Limits: Humans Language: En Journal: Med Image Anal Journal subject: DIAGNOSTICO POR IMAGEM Year: 2023 Document type: Article Affiliation country: Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Cell Nucleus Limits: Humans Language: En Journal: Med Image Anal Journal subject: DIAGNOSTICO POR IMAGEM Year: 2023 Document type: Article Affiliation country: Country of publication: