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Deep Learning in Medical Imaging: General Overview
The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep ar...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5447633/ https://www.ncbi.nlm.nih.gov/pubmed/28670152 http://dx.doi.org/10.3348/kjr.2017.18.4.570 |
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author | Lee, June-Goo Jun, Sanghoon Cho, Young-Won Lee, Hyunna Kim, Guk Bae Seo, Joon Beom Kim, Namkug |
author_facet | Lee, June-Goo Jun, Sanghoon Cho, Young-Won Lee, Hyunna Kim, Guk Bae Seo, Joon Beom Kim, Namkug |
author_sort | Lee, June-Goo |
collection | PubMed |
description | The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. |
format | Online Article Text |
id | pubmed-5447633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-54476332017-07-01 Deep Learning in Medical Imaging: General Overview Lee, June-Goo Jun, Sanghoon Cho, Young-Won Lee, Hyunna Kim, Guk Bae Seo, Joon Beom Kim, Namkug Korean J Radiol Experiment, Engineering, and Physics The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. The Korean Society of Radiology 2017 2017-05-19 /pmc/articles/PMC5447633/ /pubmed/28670152 http://dx.doi.org/10.3348/kjr.2017.18.4.570 Text en Copyright © 2017 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 | Experiment, Engineering, and Physics Lee, June-Goo Jun, Sanghoon Cho, Young-Won Lee, Hyunna Kim, Guk Bae Seo, Joon Beom Kim, Namkug Deep Learning in Medical Imaging: General Overview |
title | Deep Learning in Medical Imaging: General Overview |
title_full | Deep Learning in Medical Imaging: General Overview |
title_fullStr | Deep Learning in Medical Imaging: General Overview |
title_full_unstemmed | Deep Learning in Medical Imaging: General Overview |
title_short | Deep Learning in Medical Imaging: General Overview |
title_sort | deep learning in medical imaging: general overview |
topic | Experiment, Engineering, and Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5447633/ https://www.ncbi.nlm.nih.gov/pubmed/28670152 http://dx.doi.org/10.3348/kjr.2017.18.4.570 |
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