Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.

Paul, Rahul; Hawkins, Samuel H; Balagurunathan, Yoganand; Schabath, Matthew B; Gillies, Robert J; Hall, Lawrence O; Goldgof, Dmitry B

Paul, Rahul; Hawkins, Samuel H; Balagurunathan, Yoganand; Schabath, Matthew B; Gillies, Robert J; Hall, Lawrence O; Goldgof, Dmitry B.

Afiliação

Paul R; Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
Hawkins SH; Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
Balagurunathan Y; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
Schabath MB; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida; Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
Gillies RJ; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
Hall LO; Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.
Goldgof DB; Department of Computer Science and Engineering, University of South Florida, Tampa, Florida.

Tomography ; 2(4): 388-395, 2016 Dec.

Article em En | MEDLINE | ID: mdl-28066809

Palavras-chave

adenocarcinoma; computed tomography; deep features; deep neural network; lung cancer; pre-trained CNN; symmetric uncertainty; transfer learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Tomography Ano de publicação: 2016 Tipo de documento: Article

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