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Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.
Schelb, Patrick; Wang, Xianfeng; Radtke, Jan Philipp; Wiesenfarth, Manuel; Kickingereder, Philipp; Stenzinger, Albrecht; Hohenfellner, Markus; Schlemmer, Heinz-Peter; Maier-Hein, Klaus H; Bonekamp, David.
Affiliation
  • Schelb P; Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
  • Wang X; Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
  • Radtke JP; Department of Radiology, Affiliated Hospital of Guilin Medical University, Guangxi, Guilin, People's Republic of China.
  • Wiesenfarth M; Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
  • Kickingereder P; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany.
  • Stenzinger A; Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Hohenfellner M; Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany.
  • Schlemmer HP; Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany.
  • Maier-Hein KH; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany.
  • Bonekamp D; Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Eur Radiol ; 31(1): 302-313, 2021 Jan.
Article in En | MEDLINE | ID: mdl-32767102
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prostatic Neoplasms / Deep Learning Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans / Male Language: En Journal: Eur Radiol Journal subject: RADIOLOGIA Year: 2021 Document type: Article Affiliation country: Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prostatic Neoplasms / Deep Learning Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans / Male Language: En Journal: Eur Radiol Journal subject: RADIOLOGIA Year: 2021 Document type: Article Affiliation country: Country of publication: