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Make deep learning algorithms in computational pathology more reproducible and reusable.
Wagner, Sophia J; Matek, Christian; Shetab Boushehri, Sayedali; Boxberg, Melanie; Lamm, Lorenz; Sadafi, Ario; Waibel, Dominik J E; Marr, Carsten; Peng, Tingying.
Afiliação
  • Wagner SJ; Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
  • Matek C; Department of Informatics, Technical University of Munich, Garching, Germany.
  • Shetab Boushehri S; Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
  • Boxberg M; Institute of Pathology, University Hospital Erlangen, Erlangen, Germany.
  • Lamm L; Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
  • Sadafi A; Department of Mathematics, Technical University of Munich, Garching, Germany.
  • Waibel DJE; Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM), Penzberg, Germany.
  • Marr C; Institute of Pathology, Technical University Munich, Munich, Germany.
  • Peng T; Institute of Pathology Munich-North, Munich, Germany.
Nat Med ; 28(9): 1744-1746, 2022 09.
Article em En | MEDLINE | ID: mdl-35941376
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
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XML
PubMed Links
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Ano de publicação: 2022 Tipo de documento: Article