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Hello World Deep Learning in Medical Imaging
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5959832/ https://www.ncbi.nlm.nih.gov/pubmed/29725961 http://dx.doi.org/10.1007/s10278-018-0079-6 |
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author | Lakhani, Paras Gray, Daniel L. Pett, Carl R. Nagy, Paul Shih, George |
author_facet | Lakhani, Paras Gray, Daniel L. Pett, Carl R. Nagy, Paul Shih, George |
author_sort | Lakhani, Paras |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5959832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-59598322018-05-25 Hello World Deep Learning in Medical Imaging Lakhani, Paras Gray, Daniel L. Pett, Carl R. Nagy, Paul Shih, George J Digit Imaging Article 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. Springer International Publishing 2018-05-03 2018-06 /pmc/articles/PMC5959832/ /pubmed/29725961 http://dx.doi.org/10.1007/s10278-018-0079-6 Text en © The Author(s) 2018 Open Access This 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 Lakhani, Paras Gray, Daniel L. Pett, Carl R. Nagy, Paul Shih, George Hello World Deep Learning in Medical Imaging |
title | Hello World Deep Learning in Medical Imaging |
title_full | Hello World Deep Learning in Medical Imaging |
title_fullStr | Hello World Deep Learning in Medical Imaging |
title_full_unstemmed | Hello World Deep Learning in Medical Imaging |
title_short | Hello World Deep Learning in Medical Imaging |
title_sort | hello world deep learning in medical imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5959832/ https://www.ncbi.nlm.nih.gov/pubmed/29725961 http://dx.doi.org/10.1007/s10278-018-0079-6 |
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