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Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.
Woerl, Ann-Christin; Eckstein, Markus; Geiger, Josephine; Wagner, Daniel C; Daher, Tamas; Stenzel, Philipp; Fernandez, Aurélie; Hartmann, Arndt; Wand, Michael; Roth, Wilfried; Foersch, Sebastian.
Afiliação
  • Woerl AC; Institute of Pathology, University Medical Center Mainz, Mainz, Germany; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Eckstein M; Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Geiger J; Institute of Pathology, University Medical Center Mainz, Mainz, Germany; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Wagner DC; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
  • Daher T; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
  • Stenzel P; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
  • Fernandez A; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
  • Hartmann A; Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Wand M; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Roth W; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
  • Foersch S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany. Electronic address: sebastian.foersch@unimedizin-mainz.de.
Eur Urol ; 78(2): 256-264, 2020 08.
Article em En | MEDLINE | ID: mdl-32354610
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Bexiga Urinária / Aprendizado Profundo Idioma: En Ano de publicação: 2020 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Bexiga Urinária / Aprendizado Profundo Idioma: En Ano de publicação: 2020 Tipo de documento: Article