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How to fast and accurately assess the severity level of COVID-19 is anessential problem, when millions of people are suffering from the pandemicaround the world. Currently, the chest CT is regarded as a popular andinformative imaging tool for COVID-19 diagnosis. However, we observe that thereare two issues -- weak annotation and insufficient data that may obstructautomatic COVID-19 severity assessment with CT images. To address thesechallenges, we propose a novel three-component method, i.e., 1) a deep multipleinstance learning component with instance-level attention to jointly classifythe bag and also weigh the instances, 2) a bag-level data augmentationcomponent to generate virtual bags by reorganizing high confidential instances,and 3) a self-supervised pretext component to aid the learning process. We havesystematically evaluated our method on the CT images of 229 COVID-19 cases,including 50 severe and 179 non-severe cases. Our method could obtain anaverage accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, whichoutperformed previous works.

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning