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Validating deep learning inference during chest X-ray classification for COVID-19 screening
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patien...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352989/ https://www.ncbi.nlm.nih.gov/pubmed/34373530 http://dx.doi.org/10.1038/s41598-021-95561-y |
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author | Sadre, Robbie Sundaram, Baskaran Majumdar, Sharmila Ushizima, Daniela |
author_facet | Sadre, Robbie Sundaram, Baskaran Majumdar, Sharmila Ushizima, Daniela |
author_sort | Sadre, Robbie |
collection | PubMed |
description | The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources. |
format | Online Article Text |
id | pubmed-8352989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83529892021-08-11 Validating deep learning inference during chest X-ray classification for COVID-19 screening Sadre, Robbie Sundaram, Baskaran Majumdar, Sharmila Ushizima, Daniela Sci Rep Article The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources. Nature Publishing Group UK 2021-08-09 /pmc/articles/PMC8352989/ /pubmed/34373530 http://dx.doi.org/10.1038/s41598-021-95561-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sadre, Robbie Sundaram, Baskaran Majumdar, Sharmila Ushizima, Daniela Validating deep learning inference during chest X-ray classification for COVID-19 screening |
title | Validating deep learning inference during chest X-ray classification for COVID-19 screening |
title_full | Validating deep learning inference during chest X-ray classification for COVID-19 screening |
title_fullStr | Validating deep learning inference during chest X-ray classification for COVID-19 screening |
title_full_unstemmed | Validating deep learning inference during chest X-ray classification for COVID-19 screening |
title_short | Validating deep learning inference during chest X-ray classification for COVID-19 screening |
title_sort | validating deep learning inference during chest x-ray classification for covid-19 screening |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352989/ https://www.ncbi.nlm.nih.gov/pubmed/34373530 http://dx.doi.org/10.1038/s41598-021-95561-y |
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