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

Zhu, Lina; Gao, Ge; Zhu, Yi; Han, Chao; Liu, Xiang; Li, Derun; Liu, Weipeng; Wang, Xiangpeng; Zhang, Jingyuan; Zhang, Xiaodong; Wang, Xiaoying

Zhu, Lina; Gao, Ge; Zhu, Yi; Han, Chao; Liu, Xiang; Li, Derun; Liu, Weipeng; Wang, Xiangpeng; Zhang, Jingyuan; Zhang, Xiaodong; Wang, Xiaoying.

Afiliação

Zhu L; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Gao G; Department of Radiology, Peking University First Hospital, Beijing, China.
Zhu Y; Department of Clinical & Technical Support, Philips Healthcare, Beijing, China.
Han C; Department of Radiology, Peking University First Hospital, Beijing, China.
Liu X; Department of Radiology, Peking University First Hospital, Beijing, China.
Li D; Department of Urology, Peking University First Hospital, Beijing, China.
Liu W; Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Wang X; Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Zhang J; Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Zhang X; Department of Radiology, Peking University First Hospital, Beijing, China.
Wang X; Department of Radiology, Peking University First Hospital, Beijing, China.

Front Oncol ; 12: 958065, 2022.

Article em En | MEDLINE | ID: mdl-36249048

Palavras-chave

deep learning; detection; localization; magnetic resonance imaging; prostatic neoplasms

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies Idioma: En Revista: Front Oncol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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