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

OBJECTIVES: 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. MATERIALS AND METHODS: In this retrospective study, a previously...

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Detalles Bibliográficos
Autores principales: Netzer, Nils, Eith, Carolin, Bethge, Oliver, Hielscher, Thomas, Schwab, Constantin, Stenzinger, Albrecht, Gnirs, Regula, Schlemmer, Heinz-Peter, Maier-Hein, Klaus H., Schimmöller, Lars, Bonekamp, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598076/
https://www.ncbi.nlm.nih.gov/pubmed/37507610
http://dx.doi.org/10.1007/s00330-023-09882-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598076/
https://www.ncbi.nlm.nih.gov/pubmed/37507610
http://dx.doi.org/10.1007/s00330-023-09882-9

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