Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network

PURPOSE: To develop a cascaded deep learning model trained with apparent diffusion coefficient (ADC) and T2-weighted imaging (T2WI) for fully automated detection and localization of clinically significant prostate cancer (csPCa). METHODS: This retrospective study included 347 consecutive patients (2...

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Detalles Bibliográficos
Autores principales: Zhu, Lina, Gao, Ge, Zhu, Yi, Han, Chao, Liu, Xiang, Li, Derun, Liu, Weipeng, Wang, Xiangpeng, Zhang, Jingyuan, Zhang, Xiaodong, Wang, Xiaoying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558117/
https://www.ncbi.nlm.nih.gov/pubmed/36249048
http://dx.doi.org/10.3389/fonc.2022.958065

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558117/
https://www.ncbi.nlm.nih.gov/pubmed/36249048
http://dx.doi.org/10.3389/fonc.2022.958065

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