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Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.
Trivedi, Hari M; Panahiazar, Maryam; Liang, April; Lituiev, Dmytro; Chang, Peter; Sohn, Jae Ho; Chen, Yunn-Yi; Franc, Benjamin L; Joe, Bonnie; Hadley, Dexter.
Afiliação
  • Trivedi HM; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA. hari.trivedi@gmail.com.
  • Panahiazar M; Institute for Computational Health Sciences, University of California, San Francisco, CA, USA.
  • Liang A; University of California School of Medicine, San Francisco, CA, USA.
  • Lituiev D; Institute for Computational Health Sciences, University of California, San Francisco, CA, USA.
  • Chang P; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Sohn JH; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Chen YY; Department of Pathology, University of California, San Francisco, CA, USA.
  • Franc BL; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Joe B; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Hadley D; Institute for Computational Health Sciences, University of California, San Francisco, CA, USA.
J Digit Imaging ; 32(1): 30-37, 2019 02.
Article em En | MEDLINE | ID: mdl-30128778
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mamografia / Interpretação de Imagem Assistida por Computador / Registros Eletrônicos de Saúde / Aprendizado Profundo Limite: Female / Humans / Middle aged Idioma: En Ano de publicação: 2019 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mamografia / Interpretação de Imagem Assistida por Computador / Registros Eletrônicos de Saúde / Aprendizado Profundo Limite: Female / Humans / Middle aged Idioma: En Ano de publicação: 2019 Tipo de documento: Article