  To counter the outbreak of COVID-19, the accurate diagnosis of suspected
cases plays a crucial role in timely quarantine, medical treatment, and
preventing the spread of the pandemic. Considering the limited training cases
and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep
Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT
imaging, which only consists of two 2D CNN networks, i.e., slice- and
patient-level classification networks. The former aims to seek the feature
representations from abundant CT slices instead of limited CT volumes, and for
the overall pneumonia screening, the latter one could recover the temporal
information by feature refinement and aggregation between different slices. In
addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3
Lung-Sys also be able to locate the areas of relevant lesions, without any
pixel-level annotation. To further demonstrate the effectiveness of our model,
we conduct extensive experiments on a chest CT imaging dataset with a total of
734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and
133 CAP patients). The quantitative results with plenty of metrics indicate the
superiority of our proposed model on both slice- and patient-level
classification tasks. More importantly, the generated lesion location maps make
our system interpretable and more valuable to clinicians.
