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Hello World Deep Learning in Medical Imaging.
Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George.
Afiliação
  • Lakhani P; Department of Radiology, Sidney Kimmel Jefferson Medical College, Thomas Jefferson University Hospital, Philadelphia, PA, 19107, USA. paras.lakhani@jefferson.edu.
  • Gray DL; Sidney Kimmel Jefferson Medical College, Philadelphia, PA, USA.
  • Pett CR; Sidney Kimmel Jefferson Medical College, Philadelphia, PA, USA.
  • Nagy P; Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Shih G; Division of Health Science Informatics, Johns Hopkins University School of Public Health, Baltimore, MD, USA.
J Digit Imaging ; 31(3): 283-289, 2018 06.
Article em En | MEDLINE | ID: mdl-29725961
ABSTRACT
There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Processamento de Imagem Assistida por Computador / Diagnóstico por Imagem / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Processamento de Imagem Assistida por Computador / Diagnóstico por Imagem / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article