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Learning to detect lymphocytes in immunohistochemistry with deep learning.
Swiderska-Chadaj, Zaneta; Pinckaers, Hans; van Rijthoven, Mart; Balkenhol, Maschenka; Melnikova, Margarita; Geessink, Oscar; Manson, Quirine; Sherman, Mark; Polonia, Antonio; Parry, Jeremy; Abubakar, Mustapha; Litjens, Geert; van der Laak, Jeroen; Ciompi, Francesco.
Afiliação
  • Swiderska-Chadaj Z; Department of Pathology, Radboud University Medical Center, The Netherlands. Electronic address: zaneta.swiderska@radboudumc.nl.
  • Pinckaers H; Department of Pathology, Radboud University Medical Center, The Netherlands.
  • van Rijthoven M; Department of Pathology, Radboud University Medical Center, The Netherlands.
  • Balkenhol M; Department of Pathology, Radboud University Medical Center, The Netherlands.
  • Melnikova M; Department of Pathology, Radboud University Medical Center, The Netherlands; Department of Clinical Medicine, Aarhus University, Denmark; Institute of Pathology, Randers Regional Hospital, Denmark.
  • Geessink O; Department of Pathology, Radboud University Medical Center, The Netherlands.
  • Manson Q; Department of Pathology, University Medical Center, Utrecht, The Netherlands.
  • Sherman M; Mayo Clinic, Jacksonville, Florida, USA.
  • Polonia A; Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal.
  • Parry J; Fiona Stanley Hospital, Murdoch, Perth, Western Australia.
  • Abubakar M; Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA.
  • Litjens G; Department of Pathology, Radboud University Medical Center, The Netherlands.
  • van der Laak J; Department of Pathology, Radboud University Medical Center, The Netherlands; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
  • Ciompi F; Department of Pathology, Radboud University Medical Center, The Netherlands.
Med Image Anal ; 58: 101547, 2019 12.
Article em En | MEDLINE | ID: mdl-31476576
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imuno-Histoquímica / Linfócitos / Aprendizado Profundo Tipo de estudo: Clinical_trials / Guideline Limite: Female / Humans / Male País/Região como assunto: Europa Idioma: En Revista: Med Image Anal Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2019 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imuno-Histoquímica / Linfócitos / Aprendizado Profundo Tipo de estudo: Clinical_trials / Guideline Limite: Female / Humans / Male País/Região como assunto: Europa Idioma: En Revista: Med Image Anal Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2019 Tipo de documento: Article