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Deep learning to estimate lung disease mortality from chest radiographs.
Weiss, Jakob; Raghu, Vineet K; Bontempi, Dennis; Christiani, David C; Mak, Raymond H; Lu, Michael T; Aerts, Hugo J W L.
Afiliación
  • Weiss J; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
  • Raghu VK; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA, 02115, USA.
  • Bontempi D; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.
  • Christiani DC; Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, 02114, Boston, USA.
  • Mak RH; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
  • Lu MT; Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, 02114, Boston, USA.
  • Aerts HJWL; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
Nat Commun ; 14(1): 2797, 2023 05 16.
Article en En | MEDLINE | ID: mdl-37193717
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Enfermedades Pulmonares Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Enfermedades Pulmonares Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos