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

Zekun Li, Wei Zhao, Feng Shi, Lei Qi, Xingzhi Xie, Ying Wei, Zhongxiang Ding, Yang Gao, Shangjie Wu, Yinghuan Shi, Dinggang Shen, Jun Liu

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

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.

Original languageEnglish
Article number101978
JournalMedical Image Analysis
Volume69
DOIs
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
The work was supported by the National Key Research and Development Program of China (2019YFC0118300), National Natural Science Foundation of China (61673203, 81927808). The work was also supported by the Key Emergency Project of Pneumonia Epidemic of novel coronavirus infection (2020SK3006), Emergency Project of Prevention and Control for COVID-19 of Central South University (160260005) and Foundation from Changsha Scientific and Technical Bureau, China (kq2001001).

Publisher Copyright:
© 2021

Keywords

  • COVID-19
  • Chest CT
  • Data augmentation
  • Multiple instance learning
  • Self-supervised learning

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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