Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Mix Contrast for COVID-19 Mild-to-Critical Prediction | |
Zhu, Yongbei1,2,3; Wang, Shuo1,2,3; Wang, Siwen4; Wu, Qingxia5; Wang, Liusu1,2,3; Li, Hongjun6; Wang, Meiyun7,8; Niu, Meng9; Zha, Yunfei10,11; Tian, Jie1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING |
ISSN | 0018-9294 |
2021-06-01 | |
卷号 | 68期号:12页码:3725-3736 |
摘要 | Objective: In a few patients with mild COVID-19, there is a possibility of the infection becoming severe or critical in the future. This work aims to identify high-risk patients who have a high probability of changing from mild to critical COVID-19 (only account for 5% of cases). Methods: Using traditional convolutional neural networks for classification may not be suitable to identify this 5% of high risk patients from an entire dataset due to the highly imbalanced label distribution. To address this problem, we propose a Mix Contrast model, which matches original features with mixed features for contrastive learning. Three modules are proposed for training the model: 1) a cumulative learning strategy for synthesizing the mixed feature; 2) a commutative feature combination module for learning the commutative law of feature concatenation; 3) a united pairwise loss assigning adaptive weights for sample pairs with different class anchors based on their current optimization status. Results: We collect a multi-center computed tomography dataset including 918 confirmed COVID-19 patients from four hospitals and evaluate the proposed method on both the COVID-19 mild-to-critical prediction and COVID-19 diagnosis tasks. For mild-to-critical prediction, the experimental results show a recall of 0.80 and a specificity of 0.815. For diagnosis, the model shows comparable results with deep neural networks using a large dataset. Our method demonstrates improvements when the amount of training data is small or imbalanced. Significance: Identifying mild-to-critical COVID-19 patients is important for early prevention and personalized treatment planning. |
关键词 | Coronavirus disease 2019 (COVID-19) contrastive learning computed tomography mixup prognosis |
DOI | 10.1109/TBME.2021.3085576 |
关键词[WOS] | LUNG |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Biomedical |
WOS记录号 | WOS:000720518600030 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46453 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Tian, Jie |
作者单位 | 1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China 2.Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Northeastern Univ, Coll Med & Biomed Informat Engn, Shenyang, Peoples R China 6.Capital Med Univ, Beijing Youan Hosp, Dept Radiol, Beijing, Peoples R China 7.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou, Peoples R China 8.Zhengzhou Univ, Peoples Hosp, Zhengzhou, Peoples R China 9.China Med Univ, Dept Intervent Radiol, Hosp 1, Shenyang, Peoples R China 10.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan, Peoples R China 11.Wuhan Univ, Renmin Hosp, Dept Infect Prevent & Control Off, Wuhan, Peoples R China |
第一作者单位 | 中国科学院分子影像重点实验室 |
通讯作者单位 | 中国科学院分子影像重点实验室 |
推荐引用方式 GB/T 7714 | Zhu, Yongbei,Wang, Shuo,Wang, Siwen,et al. Mix Contrast for COVID-19 Mild-to-Critical Prediction[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2021,68(12):3725-3736. |
APA | Zhu, Yongbei.,Wang, Shuo.,Wang, Siwen.,Wu, Qingxia.,Wang, Liusu.,...&Tian, Jie.(2021).Mix Contrast for COVID-19 Mild-to-Critical Prediction.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,68(12),3725-3736. |
MLA | Zhu, Yongbei,et al."Mix Contrast for COVID-19 Mild-to-Critical Prediction".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 68.12(2021):3725-3736. |
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