Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Deep learning -based multi -view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study | |
Wu, Xiangjun1,2; Hui, Hui2,3; Niu, Meng4; Li, Liang5; Wang, Li6; He, Bingxi2,3; Yang, Xin2; Li, Li7; Li, Hongjun7; Tian, Jie2,3,8; Zha, Yunfei5 | |
发表期刊 | EUROPEAN JOURNAL OF RADIOLOGY |
ISSN | 0720-048X |
2020-07-01 | |
卷号 | 128期号:109041页码:9 |
摘要 | Purpose: To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. Methods: We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patientswithCOVID-19 using CTimageswiththe maximum lungregions inaxial,coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets. Results: The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively. Conclusions: Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia. |
关键词 | Coronavirus disease 2019 Deep learning Multi-view model Computed tomography |
DOI | 10.1016/j.ejrad.2020.109041 |
收录类别 | SCIE |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2016YFC0103803] ; National Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2019YFC0118100] ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province[2020FCA015] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81827808] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81871332] ; National Natural Science Foundation of China[81227901] ; Chinese Academy of Sciences[XDB32030200] ; Chinese Academy of Sciences[YJKYYQ20170075] |
项目资助者 | National Key Research and Development Program of China ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province ; National Natural Science Foundation of China ; Chinese Academy of Sciences |
WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000540453900003 |
出版者 | ELSEVIER IRELAND LTD |
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/173211/39841 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Niu, Meng; Tian, Jie; Zha, Yunfei |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.China Med Univ, Hosp 1, Intervent Radiol Dept, Shenyang 110001, Peoples R China 5.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan 430060, Peoples R China 6.Wuhan Univ, Renmin Hosp, Dept Infect Prevent & Control Off, Wuhan 430060, Peoples R China 7.Capital Med Univ, Beijing Youan Hosp, Dept Radiol, Beijing 100069, Peoples R China 8.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100083, 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 | Wu, Xiangjun,Hui, Hui,Niu, Meng,et al. Deep learning -based multi -view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study[J]. EUROPEAN JOURNAL OF RADIOLOGY,2020,128(109041):9. |
APA | Wu, Xiangjun.,Hui, Hui.,Niu, Meng.,Li, Liang.,Wang, Li.,...&Zha, Yunfei.(2020).Deep learning -based multi -view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.EUROPEAN JOURNAL OF RADIOLOGY,128(109041),9. |
MLA | Wu, Xiangjun,et al."Deep learning -based multi -view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study".EUROPEAN JOURNAL OF RADIOLOGY 128.109041(2020):9. |
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