Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective.

Yedururi, Sireesha; Morani, Ajaykumar C; Katabathina, Venkata Subbiah; Jo, Nahyun; Rachamallu, Medhini; Prasad, Srinivasa; Marcal, Leonardo

Yedururi, Sireesha; Morani, Ajaykumar C; Katabathina, Venkata Subbiah; Jo, Nahyun; Rachamallu, Medhini; Prasad, Srinivasa; Marcal, Leonardo.

Afiliación

Yedururi S; From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.
Morani AC; From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.
Katabathina VS; Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX.
Jo N; Department of Internal Medicine, UAB Montgomery Regional Medical Campus, Montogomery, AL.
Rachamallu M; Department of Biomedical Engineering, The University of Virginia, VA.
Prasad S; From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.
Marcal L; From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.

J Comput Assist Tomogr ; 45(6): 805-811, 2021.

Article en En | MEDLINE | ID: mdl-34270486

Asunto(s)

Neoplasias Abdominales/diagnóstico por imagen; Diagnóstico por Imagen/métodos; Interpretación de Imagen Asistida por Computador/métodos; Aprendizaje Automático; Abdomen/diagnóstico por imagen; Humanos

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diagnóstico por Imagen / Interpretación de Imagen Asistida por Computador / Aprendizaje Automático / Neoplasias Abdominales Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: J Comput Assist Tomogr Año: 2021 Tipo del documento: Article

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