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An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recordedin Wuhan, China since late December 2019, which subsequently became pandemicaround the world. Although COVID-19 is an acutely treated disease, it can alsobe fatal with a risk of fatality of 4.03% in China and the highest of 13.04% inAlgeria and 12.67% Italy (as of 8th April 2020). The onset of serious illnessmay result in death as a consequence of substantial alveolar damage andprogressive respiratory failure. Although laboratory testing, e.g., usingreverse transcription polymerase chain reaction (RT-PCR), is the goldenstandard for clinical diagnosis, the tests may produce false negatives.Moreover, under the pandemic situation, shortage of RT-PCR testing resourcesmay also delay the following clinical decision and treatment. Under suchcircumstances, chest CT imaging has become a valuable tool for both diagnosisand prognosis of COVID-19 patients. In this study, we propose a weaklysupervised deep learning strategy for detecting and classifying COVID-19infection from CT images. The proposed method can minimise the requirements ofmanual labelling of CT images but still be able to obtain accurate infectiondetection and distinguish COVID-19 from non-COVID-19 cases. Based on thepromising results obtained qualitatively and quantitatively, we can envisage awide deployment of our developed technique in large-scale clinical studies.

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images