Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability

To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.

Bibliographic Details

Main Authors:	Netzer, Nils (Author) , Eith, Carolin (Author) , Bethge, Oliver (Author) , Hielscher, Thomas (Author) , Schwab, Constantin (Author) , Stenzinger, Albrecht (Author) , Gnirs, Regula (Author) , Schlemmer, Heinz-Peter (Author) , Maier-Hein, Klaus H. (Author) , Schimmöller, Lars (Author) , Bonekamp, David (Author)
Format:	Article (Journal)
Language:	English
Published:	28 July 2023
In:	European radiology Year: 2023, Volume: 33, Issue: 11, Pages: 7463-7476
ISSN:	1432-1084
DOI:	10.1007/s00330-023-09882-9
Online Access:	Resolving-System, kostenfrei, Volltext: https://doi.org/10.1007/s00330-023-09882-9 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00330-023-09882-9
Author Notes:	Nils Netzer, Carolin Eith, Oliver Bethge, Thomas Hielscher, Constantin Schwab, Albrecht Stenzinger, Regula Gnirs, Heinz-Peter Schlemmer, Klaus H. Maier-Hein, Lars Schimmöller, David Bonekamp