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Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models.
Hong, Runyu; Liu, Wenke; DeLair, Deborah; Razavian, Narges; Fenyö, David.
Affiliation
  • Hong R; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA.
  • Liu W; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
  • DeLair D; Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA.
  • Razavian N; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
  • Fenyö D; Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA.
Cell Rep Med ; 2(9): 100400, 2021 09 21.
Article in En | MEDLINE | ID: mdl-34622237
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Endometrial Neoplasms / Imaging, Three-Dimensional Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans Language: En Journal: Cell Rep Med Year: 2021 Document type: Article Affiliation country: Estados Unidos
Fulltext
Add to My VHL
Print
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Endometrial Neoplasms / Imaging, Three-Dimensional Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans Language: En Journal: Cell Rep Med Year: 2021 Document type: Article Affiliation country: Estados Unidos