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

TL;DR

This paper introduces a novel multiple instance learning framework that combines data augmentation and self-supervised learning to improve COVID-19 severity assessment from chest CT images, addressing issues of weak annotation and limited data.

Contribution

The paper presents a new three-component method integrating deep multiple instance learning, data augmentation, and self-supervised learning for COVID-19 severity classification.

Findings

Achieved 95.8% accuracy on COVID-19 severity assessment

Outperformed previous methods in sensitivity and specificity

Effectively addressed data scarcity and annotation issues

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.