A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
DC Field | Value | Language |
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dc.contributor.author | Li, Zekun | - |
dc.contributor.author | Zhao, Wei | - |
dc.contributor.author | Shi, Feng | - |
dc.contributor.author | Qi, Lei | - |
dc.contributor.author | Xie, Xingzhi | - |
dc.contributor.author | Wei, Ying | - |
dc.contributor.author | Ding, Zhongxiang | - |
dc.contributor.author | Gao, Yang | - |
dc.contributor.author | Wu, Shangjie | - |
dc.contributor.author | Liu, Jun | - |
dc.contributor.author | Shi, Yinghuan | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2022-03-04T00:41:33Z | - |
dc.date.available | 2022-03-04T00:41:33Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 1361-8415 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137684 | - |
dc.description.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. (c) 2021 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1016/j.media.2021.101978 | - |
dc.identifier.scopusid | 2-s2.0-85100692794 | - |
dc.identifier.wosid | 000639618600001 | - |
dc.identifier.bibliographicCitation | MEDICAL IMAGE ANALYSIS, v.69 | - |
dc.relation.isPartOf | MEDICAL IMAGE ANALYSIS | - |
dc.citation.title | MEDICAL IMAGE ANALYSIS | - |
dc.citation.volume | 69 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | Chest CT | - |
dc.subject.keywordAuthor | Data augmentation | - |
dc.subject.keywordAuthor | Multiple instance learning | - |
dc.subject.keywordAuthor | Self-supervised learning | - |
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