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Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data
The trendy task of digital medical image analysis has been continually evolving. It has been an area of prominent and growing importance from both research and deployment perspectives. Nonetheless, it is necessary to realize that the use of algorithms, methodology, as well as the source of medical i...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759826/ https://www.ncbi.nlm.nih.gov/pubmed/34090971 http://dx.doi.org/10.1016/j.ymeth.2021.06.002 |
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author | Chan, Jonathan H. Li, Chenqi |
author_facet | Chan, Jonathan H. Li, Chenqi |
author_sort | Chan, Jonathan H. |
collection | PubMed |
description | The trendy task of digital medical image analysis has been continually evolving. It has been an area of prominent and growing importance from both research and deployment perspectives. Nonetheless, it is necessary to realize that the use of algorithms, methodology, as well as the source of medical image data, must be strictly scrutinized. As the COVID-19 pandemic has been gripping much of the world recently, there has been much efforts gone into developing affordable testing for the masses, and it has been shown that the established and widely available chest X-rays (CXR) images may be used as a screening criteria for assistive diagnosis purpose. Thanks to the dedicated work by various individuals and organizations, publicly available CXR of COVID-19 subjects are available for analytic usage. We have also provided a publicly available CXR dataset on the Kaggle platform. As a case study, this paper presents a systematic approach to learn from a typically imbalanced set of CXR images, which consists of a limited number of publicly available COVID-19 images. Our results show that we are able to outperform the top finishers in a related Kaggle multi-class CXR challenge. The proposed methodology should be able to help guide medical personnel in obtaining a robust diagnosis model to discern COVID-19 from other conditions confidently. |
format | Online Article Text |
id | pubmed-9759826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97598262022-12-19 Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data Chan, Jonathan H. Li, Chenqi Methods Article The trendy task of digital medical image analysis has been continually evolving. It has been an area of prominent and growing importance from both research and deployment perspectives. Nonetheless, it is necessary to realize that the use of algorithms, methodology, as well as the source of medical image data, must be strictly scrutinized. As the COVID-19 pandemic has been gripping much of the world recently, there has been much efforts gone into developing affordable testing for the masses, and it has been shown that the established and widely available chest X-rays (CXR) images may be used as a screening criteria for assistive diagnosis purpose. Thanks to the dedicated work by various individuals and organizations, publicly available CXR of COVID-19 subjects are available for analytic usage. We have also provided a publicly available CXR dataset on the Kaggle platform. As a case study, this paper presents a systematic approach to learn from a typically imbalanced set of CXR images, which consists of a limited number of publicly available COVID-19 images. Our results show that we are able to outperform the top finishers in a related Kaggle multi-class CXR challenge. The proposed methodology should be able to help guide medical personnel in obtaining a robust diagnosis model to discern COVID-19 from other conditions confidently. Elsevier Inc. 2022-06 2021-06-04 /pmc/articles/PMC9759826/ /pubmed/34090971 http://dx.doi.org/10.1016/j.ymeth.2021.06.002 Text en © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Chan, Jonathan H. Li, Chenqi Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data |
title | Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data |
title_full | Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data |
title_fullStr | Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data |
title_full_unstemmed | Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data |
title_short | Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data |
title_sort | learning from imbalanced covid-19 chest x-ray (cxr) medical imaging data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759826/ https://www.ncbi.nlm.nih.gov/pubmed/34090971 http://dx.doi.org/10.1016/j.ymeth.2021.06.002 |
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