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Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features.
Paul, Rahul; Schabath, Matthew; Balagurunathan, Yoganand; Liu, Ying; Li, Qian; Gillies, Robert; Hall, Lawrence O; Goldgof, Dmitry B.
Afiliação
  • Paul R; Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
  • Schabath M; Department of Cancer Epidemiology, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL.
  • Balagurunathan Y; Department of Cancer Imaging and Metabolism, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL; and.
  • Liu Y; Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin.
  • Li Q; Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin.
  • Gillies R; Department of Cancer Imaging and Metabolism, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL; and.
  • Hall LO; Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
  • Goldgof DB; Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
Tomography ; 5(1): 192-200, 2019 03.
Article em En | MEDLINE | ID: mdl-30854457
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Interpretação de Imagem Radiográfica Assistida por Computador / Nódulo Pulmonar Solitário / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Tomography Ano de publicação: 2019 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: Interpretação de Imagem Radiográfica Assistida por Computador / Nódulo Pulmonar Solitário / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Tomography Ano de publicação: 2019 Tipo de documento: Article