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  • [\'Department of Mathematics, Nanjing University of Science and Technology, Nanjing, 210094, P. R. China.\', \'Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100190, P. R. China.\', \'China Electronics Cloud Brain (Tianjin) Technology CO., Ltd, Tianjin, 300309, P. R. China.\', \'Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, 213001, P. R. China.\', \'Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, P. R. China.\', \'Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.\', \'Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, P. R. China.\', \'Lenovo Ltd., Beijing, 100094, P. R. China.\', \'Department of Mathematics, Nanjing University, Nanjing, 210093, P. R. China.\']
?:citedBy
  • -1
?:creator
?:doi
?:doi
  • 10.1002/mp.14676
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?:journal
  • Medical physics
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  • 33354790
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is ?:relation_isRelatedTo_publication of
?:title
  • Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.
?:type
?:year
  • 2021

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