Weakly supervised deep learning for COVID-19 infection detection and classification from CT images

• DOI: 10.48550/arxiv.2004.06689
• Publication Date: 2020-01-01
• Authors: Hu, Shaoping; Gao, Yuan; Niu, Zhangming; Jiang, Yinghui; Li, Lao; Xiao, Xianglu; Wang, Minhao; Fang, Evandro Fei; Menpes-Smith, Wade; Xia, Jun; Ye, Hui; Yang, Guang
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; General Computer Science; Computer Vision and Pattern Recognition (cs.CV); cs.LG; Computer Science - Computer Vision and Pattern Recognition; 610; convolutional neural network; 02 engineering and technology; 09 Engineering; Machine Learning (cs.LG); 03 medical and health sciences; 0302 clinical medicine; 10 Technology; CT~images; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; General Materials Science; cs.CV; weakly supervision; Image and Video Processing (eess.IV); General Engineering; COVID-19; deep learning; Electrical Engineering and Systems Science - Image and Video Processing; TK1-9971; 3. Good health; classification; eess.IV; 08 Information and Computing Sciences; Electrical engineering. Electronics. Nuclear engineering

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.<br/>21 pages, 7 figures<br/>