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Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning

Half a century ago, the term “computer-aided diagnosis” (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant techn...

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Detalles Bibliográficos
Autor principal: van Ginneken, Bram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Japan 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337239/
https://www.ncbi.nlm.nih.gov/pubmed/28211015
http://dx.doi.org/10.1007/s12194-017-0394-5
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author van Ginneken, Bram
author_facet van Ginneken, Bram
author_sort van Ginneken, Bram
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description Half a century ago, the term “computer-aided diagnosis” (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.
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spelling pubmed-53372392017-03-17 Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning van Ginneken, Bram Radiol Phys Technol Article Half a century ago, the term “computer-aided diagnosis” (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest. Springer Japan 2017-02-16 2017 /pmc/articles/PMC5337239/ /pubmed/28211015 http://dx.doi.org/10.1007/s12194-017-0394-5 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
van Ginneken, Bram
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
title Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
title_full Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
title_fullStr Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
title_full_unstemmed Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
title_short Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
title_sort fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337239/
https://www.ncbi.nlm.nih.gov/pubmed/28211015
http://dx.doi.org/10.1007/s12194-017-0394-5
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