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

Source: [A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning](<https://www.sota2.com/research/paper/68fa69341e209131cea20ced>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:55:51.498Z

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

Published: 2021-02-07T16:30:18.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [COVID-19 Mortality Prediction on SnapCXR dataset (test)](<https://www.sota2.com/research/sota/covid-19-mortality-prediction-on-snapcxr-dataset-test.md>): COVID-19 Mortality Prediction; SnapCXR dataset (test); AUC: 81.

- [COVID-19 Ventilation Prediction on SnapCXR (test)](<https://www.sota2.com/research/sota/covid-19-ventilation-prediction-on-snapcxr-test.md>): COVID-19 Ventilation Prediction; SnapCXR (test); AUC: 0.79.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2102.03837>)

[PDF](<https://arxiv.org/pdf/2102.03837.pdf>)