  How to fast and accurately assess the severity level of COVID-19 is an
essential problem, when millions of people are suffering from the pandemic
around the world. Currently, the chest CT is regarded as a popular and
informative imaging tool for COVID-19 diagnosis. However, we observe that there
are two issues -- weak annotation and insufficient data that may obstruct
automatic COVID-19 severity assessment with CT images. To address these
challenges, we propose a novel three-component method, i.e., 1) a deep multiple
instance learning component with instance-level attention to jointly classify
the bag and also weigh the instances, 2) a bag-level data augmentation
component to generate virtual bags by reorganizing high confidential instances,
and 3) a self-supervised pretext component to aid the learning process. We have
systematically evaluated our method on the CT images of 229 COVID-19 cases,
including 50 severe and 179 non-severe cases. Our method could obtain an
average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which
outperformed previous works.
