Your browser doesn't support javascript.
loading
Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer.
Zou, Yuan; Cai, Lingkai; Chen, Chunxiao; Shao, Qiang; Fu, Xue; Yu, Jie; Wang, Liang; Chen, Zhiying; Yang, Xiao; Yuan, Baorui; Liu, Peikun; Lu, Qiang.
Afiliação
  • Zou Y; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Cai L; Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Chen C; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. Electronic address: ccxbme@nuaa.edu.cn.
  • Shao Q; Department of Urology, the Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing, China. Electronic address: sq7166822@163.com.
  • Fu X; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Yu J; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Wang L; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Chen Z; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Yang X; Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Yuan B; Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Liu P; Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Lu Q; Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China. Electronic address: doctorlvqiang@sina.com.
Comput Biol Med ; 151(Pt A): 106219, 2022 12.
Article em En | MEDLINE | ID: mdl-36343408
Assuntos
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Bexiga Urinária / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Comput Biol Med Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Bexiga Urinária / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Comput Biol Med Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China