Your browser doesn't support javascript.
loading
Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets.
Gresser, Eva; Schachtner, Balthasar; Stüber, Anna Theresa; Solyanik, Olga; Schreier, Andrea; Huber, Thomas; Froelich, Matthias Frank; Magistro, Giuseppe; Kretschmer, Alexander; Stief, Christian; Ricke, Jens; Ingrisch, Michael; Nörenberg, Dominik.
Afiliação
  • Gresser E; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Schachtner B; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Stüber AT; Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany.
  • Solyanik O; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Schreier A; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Huber T; Department of Otolaryngology, University Hospital, LMU Munich, Munich, Germany.
  • Froelich MF; Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Mannheim, Germany.
  • Magistro G; Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Mannheim, Germany.
  • Kretschmer A; Department of Urology, University Hospital, LMU Munich, Munich, Germany.
  • Stief C; Department of Urology, University Hospital, LMU Munich, Munich, Germany.
  • Ricke J; Department of Urology, University Hospital, LMU Munich, Munich, Germany.
  • Ingrisch M; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Nörenberg D; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
Quant Imaging Med Surg ; 12(11): 4990-5003, 2022 Nov.
Article em En | MEDLINE | ID: mdl-36330197

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article