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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...
Autores principales: | Karagoz, Ahmet, Alis, Deniz, Seker, Mustafa Ege, Zeybel, Gokberk, Yergin, Mert, Oksuz, Ilkay, Karaarslan, Ercan |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2023
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Materias: | |
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 |
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