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

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

Abstract 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 learn...

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Bibliografische gegevens
Hoofdauteurs: Ahmet Karagoz, Deniz Alis, Mustafa Ege Seker, Gokberk Zeybel, Mert Yergin, Ilkay Oksuz, Ercan Karaarslan
Formaat: Artikel
Taal:English
Gepubliceerd in: SpringerOpen 2023-06-01
Reeks:Insights into Imaging
Onderwerpen:
Deep learning
Magnetic resonance imaging
Prostate cancer
Online toegang:https://doi.org/10.1186/s13244-023-01439-0

Internet

https://doi.org/10.1186/s13244-023-01439-0

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