Deep Feature Stability Analysis Using CT Images of a Physical Phantom Across Scanner Manufacturers, Cartridges, Pixel Sizes, and Slice Thickness.

Paul, Rahul; Shafiq-Ul Hassan, Mohammed; Moros, Eduardo G; Gillies, Robert J; Hall, Lawrence O; Goldgof, Dmitry B

Paul, Rahul; Shafiq-Ul Hassan, Mohammed; Moros, Eduardo G; Gillies, Robert J; Hall, Lawrence O; Goldgof, Dmitry B.

Afiliación

Paul R; Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
Shafiq-Ul Hassan M; Department of Therapeutic Radiology, Yale School of Medicine, Yale University, New Haven, CT.
Moros EG; Departments of Cancer Physiology; and.
Gillies RJ; Radiation Oncology, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL.
Hall LO; Departments of Cancer Physiology; and.
Goldgof DB; Department of Computer Science and Engineering, University of South Florida, Tampa, FL.

Tomography ; 6(2): 250-260, 2020 06.

Article en En | MEDLINE | ID: mdl-32548303

Asunto(s)

Carcinoma de Pulmón de Células no Pequeñas; Neoplasias Pulmonares; Tomografía Computarizada por Rayos X; Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen; Humanos; Redes Neurales de la Computación; Fantasmas de Imagen

Palabras clave

NSCLC; Phantom; convolutional neural network; deep feature; radiomics; transfer learning

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Tomography Año: 2020 Tipo del documento: Article

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