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.
@article{arxiv.2102.03837,
title = {A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning},
author = {Zekun Li and Wei Zhao and Feng Shi and Lei Qi and Xingzhi Xie and Ying Wei and Zhongxiang Ding and Yang Gao and Shangjie Wu and Jun Liu and Yinghuan Shi and Dinggang Shen},
journal= {arXiv preprint arXiv:2102.03837},
year = {2021}
}