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Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.
Sun, Kailun; Fan, Chanyuan; Feng, Zhaoyan; Min, Xiangde; Wang, Yu; Sun, Ziyan; Li, Yan; Cai, Wei; Yin, Xi; Zhang, Peipei; Liu, Qiuyu; Xia, Liming.
Afiliación
  • Sun K; Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Fan C; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Feng Z; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Min X; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Wang Y; Department of Research and Development, Infervision Medical Technology Co., Ltd., Beijing, China.
  • Sun Z; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Li Y; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Cai W; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Yin X; Department of CT & MRI, The First Affiliated Hospital, College of Medicine, Shihezi University, Shihezi, China.
  • Zhang P; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Liu Q; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Xia L; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Front Med (Lausanne) ; 10: 1277535, 2023.
Article en En | MEDLINE | ID: mdl-37795413
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza