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is ?:annotates of
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  • [\'Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA.\', \'Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA; Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD USA.\', \'Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA.\', \'Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA.\', \'Radiology and Imaging Sciences, NIH Clinical Center, National Institutes of Health, Bethesda, MD, USA.\', \'Radiology Department, Fondazione IRCCS Cá Granda Ospedale Maggiore Policlinico, University of Milan, Italy.\', \'Diagnostic and Interventional Radiology Service, San Paolo Hospital; ASST Santi Paolo e Carlo, Milan, Italy.\', \'Self-Defense Forces Central Hospital, Tokyo, Japan.\', \'Department of Radiology, Xiangyang First People\'s Hospital Affiliated to Hubei University of Medicine Xiangyang, Hubei, China.\', \'Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA. Electronic address: daguangx@nvidia.com.\']
?:citedBy
  • -1
?:creator
?:doi
?:doi
  • S1361-8415(21)00038-410.1016/j.media.2021.101992
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  • Medical image analysis
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?:pmid
?:pmid
  • 33601166
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  • 1.928
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  • 105
?:title
  • Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.
?:type
?:year
  • 2021

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