  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.
