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Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization.
Luo, Yong-Heng; Xi, Ianto Lin; Wang, Robin; Abdallah, Hatem Omar; Wu, Jing; Vance, Ansar Z; Chang, Ken; Kohi, Maureen; Jones, Lisa; Reddy, Shilpa; Zhang, Zi-Shu; Bai, Harrison X; Shlansky-Goldberg, Richard.
Afiliação
  • Luo YH; Department of Radiology, The Second Xiangya Hospital of Central South University, 139 Renming Middle Road, Changsha, Hunan, China.
  • Xi IL; Division of Interventional Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Wang R; Division of Interventional Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Abdallah HO; Division of Interventional Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Wu J; Department of Radiology, The Second Xiangya Hospital of Central South University, 139 Renming Middle Road, Changsha, Hunan, China.
  • Vance AZ; Division of Interventional Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Chang K; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Kohi M; Department of Radiology and Biomedical Imaging, University of California, San Francisco, California.
  • Jones L; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Reddy S; Division of Interventional Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Zhang ZS; Department of Radiology, The Second Xiangya Hospital of Central South University, 139 Renming Middle Road, Changsha, Hunan, China. Electronic address: zishuzhang@csu.edu.cn.
  • Bai HX; Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, Rhode Island.
  • Shlansky-Goldberg R; Division of Interventional Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
J Vasc Interv Radiol ; 31(6): 1010-1017.e3, 2020 Jun.
Article em En | MEDLINE | ID: mdl-32376183
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Uterinas / Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Diagnóstico por Computador / Embolização da Artéria Uterina / Aprendizado Profundo / Leiomioma Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Middle aged País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Uterinas / Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Diagnóstico por Computador / Embolização da Artéria Uterina / Aprendizado Profundo / Leiomioma Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Middle aged País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article