Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study

OBJECTIVE: To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning. MET...

Descripción completa

Detalles Bibliográficos
Autores principales: Karagoz, Ahmet, Alis, Deniz, Seker, Mustafa Ege, Zeybel, Gokberk, Yergin, Mert, Oksuz, Ilkay, Karaarslan, Ercan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10279591/
https://www.ncbi.nlm.nih.gov/pubmed/37337101
http://dx.doi.org/10.1186/s13244-023-01439-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10279591/
https://www.ncbi.nlm.nih.gov/pubmed/37337101
http://dx.doi.org/10.1186/s13244-023-01439-0

Ejemplares similares