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

• URL: http://arxiv.org/abs/2102.03837v1
• Date: Sun, 7 Feb 2021 16:30:18 GMT
• Title: A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
• Authors: Zekun Li, Wei Zhao, Feng Shi, Lei Qi, Xingzhi Xie, Ying Wei, Zhongxiang Ding, Yang Gao, Shangjie Wu, Jun Liu, Yinghuan Shi, Dinggang Shen
• Abstract summary: 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. We observe that there are two issues -- weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works.
• Score: 64.90342559393275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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