EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography.

Lin, Yannan; Wei, Leihao; Han, Simon X; Aberle, Denise R; Hsu, William

Lin, Yannan; Wei, Leihao; Han, Simon X; Aberle, Denise R; Hsu, William.

Afiliación

Lin Y; Department of Bioengineering, University of California, Los Angeles, CA, USA.
Wei L; Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Han SX; Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Aberle DR; Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Hsu W; Department of Bioengineering, University of California, Los Angeles, CA, USA.

Proc SPIE Int Soc Opt Eng ; 113142020 Feb.

Article en En | MEDLINE | ID: mdl-32606487

Palabras clave

computed tomography; computer-aided diagnosis; deep learning; pulmonary nodule classification; pulmonary nodule detection

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Proc SPIE Int Soc Opt Eng Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

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