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Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. H...

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Detalles Bibliográficos
Autores principales: Do, Synho, Song, Kyoung Doo, Chung, Joo Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960318/
https://www.ncbi.nlm.nih.gov/pubmed/31920027
http://dx.doi.org/10.3348/kjr.2019.0312
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author Do, Synho
Song, Kyoung Doo
Chung, Joo Won
author_facet Do, Synho
Song, Kyoung Doo
Chung, Joo Won
author_sort Do, Synho
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description Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists' understanding.
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spelling pubmed-69603182020-01-22 Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning Do, Synho Song, Kyoung Doo Chung, Joo Won Korean J Radiol Technology, Experiment, and Physics Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists' understanding. The Korean Society of Radiology 2020-01 2019-12-06 /pmc/articles/PMC6960318/ /pubmed/31920027 http://dx.doi.org/10.3348/kjr.2019.0312 Text en Copyright © 2020 The Korean Society of Radiology http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technology, Experiment, and Physics
Do, Synho
Song, Kyoung Doo
Chung, Joo Won
Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning
title Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning
title_full Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning
title_fullStr Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning
title_full_unstemmed Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning
title_short Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning
title_sort basics of deep learning: a radiologist's guide to understanding published radiology articles on deep learning
topic Technology, Experiment, and Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960318/
https://www.ncbi.nlm.nih.gov/pubmed/31920027
http://dx.doi.org/10.3348/kjr.2019.0312
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