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A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation.
Ko, Hoon; Huh, Jimi; Kim, Kyung Won; Chung, Heewon; Ko, Yousun; Kim, Jai Keun; Lee, Jei Hee; Lee, Jinseok.
Afiliação
  • Ko H; Department of Biomedical Engineering, Kyung Hee University, Yongin-si, Republic of Korea.
  • Huh J; The Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Kim KW; Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Chung H; Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Ko Y; Department of Biomedical Engineering, Kyung Hee University, Yongin-si, Republic of Korea.
  • Kim JK; Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.
  • Lee JH; The Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Lee J; The Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
J Med Internet Res ; 24(1): e34415, 2022 01 03.
Article em En | MEDLINE | ID: mdl-34982041
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article