Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images
An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9127422/ |
| _version_ | 1819295719579713536 |
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| author | Shaoping Hu Yuan Gao Zhangming Niu Yinghui Jiang Lao Li Xianglu Xiao Minhao Wang Evandro Fei Fang Wade Menpes-Smith Jun Xia Hui Ye Guang Yang |
| author_facet | Shaoping Hu Yuan Gao Zhangming Niu Yinghui Jiang Lao Li Xianglu Xiao Minhao Wang Evandro Fei Fang Wade Menpes-Smith Jun Xia Hui Ye Guang Yang |
| author_sort | Shaoping Hu |
| collection | DOAJ |
| description | An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies. |
| first_indexed | 2024-12-24T04:46:41Z |
| format | Article |
| id | doaj.art-33ff80bdfafb4b9ebd7010bede8fd636 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:46:41Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-33ff80bdfafb4b9ebd7010bede8fd6362022-12-21T17:14:40ZengIEEEIEEE Access2169-35362020-01-01811886911888310.1109/ACCESS.2020.30055109127422Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT ImagesShaoping Hu0Yuan Gao1Zhangming Niu2Yinghui Jiang3Lao Li4Xianglu Xiao5Minhao Wang6Evandro Fei Fang7Wade Menpes-Smith8Jun Xia9Hui Ye10Guang Yang11https://orcid.org/0000-0001-7344-7733Radiology Department, Hospital of Wuhan Red Cross Society, Wuhan, ChinaInstitute of Biomedical Engineering, University of Oxford, Oxford, U.KAladdin Healthcare Technologies Ltd., London, U.KHangzhou Ocean’s Smart Boya Company, Ltd., Hangzhou, ChinaHangzhou Ocean’s Smart Boya Company, Ltd., Hangzhou, ChinaAladdin Healthcare Technologies Ltd., London, U.KHangzhou Ocean’s Smart Boya Company, Ltd., Hangzhou, ChinaDepartment of Clinical Molecular Biology, University of Oslo, Oslo, NorwayAladdin Healthcare Technologies Ltd., London, U.KRadiology Department, Shenzhen Second People’s Hospital, Shenzhen, ChinaPET-CT Center, Hunan Cancer Hospital, Changsha, ChinaNHLI, Imperial College London, London, U.KAn outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.https://ieeexplore.ieee.org/document/9127422/COVID-19deep learningweakly supervisionCT~imagesclassificationconvolutional neural network |
| spellingShingle | Shaoping Hu Yuan Gao Zhangming Niu Yinghui Jiang Lao Li Xianglu Xiao Minhao Wang Evandro Fei Fang Wade Menpes-Smith Jun Xia Hui Ye Guang Yang Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images IEEE Access COVID-19 deep learning weakly supervision CT~images classification convolutional neural network |
| title | Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images |
| title_full | Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images |
| title_fullStr | Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images |
| title_full_unstemmed | Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images |
| title_short | Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images |
| title_sort | weakly supervised deep learning for covid 19 infection detection and classification from ct images |
| topic | COVID-19 deep learning weakly supervision CT~images classification convolutional neural network |
| url | https://ieeexplore.ieee.org/document/9127422/ |
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