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Multi-modal Learning with Missing Data for Cancer Diagnosis Using Histopathological and Genomic Data.
Cui, Can; Asad, Zuhayr; Dean, William F; Smith, Isabelle T; Madden, Christopher; Bao, Shunxing; Landman, Bennett A; Roland, Joseph T; Coburn, Lori A; Wilson, Keith T; Zwerner, Jeffrey P; Zhao, Shilin; Wheless, Lee E; Huo, Yuankai.
Afiliação
  • Cui C; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
  • Asad Z; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
  • Dean WF; College of Arts and Science, Vanderbilt University, Nashville, TN 37235, USA.
  • Smith IT; College of Arts and Science, Vanderbilt University, Nashville, TN 37235, USA.
  • Madden C; College of Medicine, SUNY Downstate Health Science University, Brooklyn, NY 11203, USA.
  • Bao S; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA.
  • Landman BA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA.
  • Roland JT; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
  • Coburn LA; Department of Surgery, Vanderbilt University Medical Center, Nashville, TN 37215, USA.
  • Wilson KT; Division of Gastroenterology Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37215, USA.
  • Zwerner JP; Division of Gastroenterology Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37215, USA.
  • Zhao S; Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN 37215, USA.
  • Wheless LE; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37215, USA.
  • Huo Y; Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN 37215, USA.
Article em En | MEDLINE | ID: mdl-36304178

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article