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COVID-19 detection based on self-supervised transfer learning using chest X-ray images
PURPOSE: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiologi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9765379/ https://www.ncbi.nlm.nih.gov/pubmed/36538184 http://dx.doi.org/10.1007/s11548-022-02813-x |
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author | Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki |
author_facet | Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki |
author_sort | Li, Guang |
collection | PubMed |
description | PURPOSE: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient’s care to help saturated medical facilities in a pandemic situation. METHODS: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. RESULTS: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. CONCLUSIONS: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers. |
format | Online Article Text |
id | pubmed-9765379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-97653792022-12-21 COVID-19 detection based on self-supervised transfer learning using chest X-ray images Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki Int J Comput Assist Radiol Surg Original Article PURPOSE: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient’s care to help saturated medical facilities in a pandemic situation. METHODS: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. RESULTS: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. CONCLUSIONS: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers. Springer International Publishing 2022-12-20 2023 /pmc/articles/PMC9765379/ /pubmed/36538184 http://dx.doi.org/10.1007/s11548-022-02813-x Text en © CARS 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Li, Guang Togo, Ren Ogawa, Takahiro Haseyama, Miki COVID-19 detection based on self-supervised transfer learning using chest X-ray images |
title | COVID-19 detection based on self-supervised transfer learning using chest X-ray images |
title_full | COVID-19 detection based on self-supervised transfer learning using chest X-ray images |
title_fullStr | COVID-19 detection based on self-supervised transfer learning using chest X-ray images |
title_full_unstemmed | COVID-19 detection based on self-supervised transfer learning using chest X-ray images |
title_short | COVID-19 detection based on self-supervised transfer learning using chest X-ray images |
title_sort | covid-19 detection based on self-supervised transfer learning using chest x-ray images |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9765379/ https://www.ncbi.nlm.nih.gov/pubmed/36538184 http://dx.doi.org/10.1007/s11548-022-02813-x |
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