Survey of Studies of COVID-19 Diagnosis Based on Deep Learning

doi:10.19678/j.issn.1000-3428.0060509

Abstract

Abstract: The Corona Virus Disease 2019(COVID-19) is highly infectious and pathogenic,posing a serious threat to public safety.Rapid and accurate detection and diagnosis of COVID-19 is key to the epidemic control.The existing detection and diagnosis methods are mainly based on nucleic acid tests or manual diagnosis using medical images.However,nucleic acid tests are time-consuming and require special test boxes,while the manual diagnosis relies heavily on professional knowledge,takes longer time for analysis and often fail to detect concealed lesions.Since then,with the development of X-ray and Computer Tomography(CT) image datasets,researchers have built many deep learning-based COVID-19 detection and diagnosis models which effectively assist medical experts in the efficient diagnosis and treatment of COVID-19.This paper lists the mainstream image datasets for the detection and diagnosis of COVID-19 and related evaluation metrics.Then,it introduces the existing deep learning-based models for COVID-19 diagnosis from the perspectives of the model task and the image data type,and on this basis compares and analyzes the detection performance of the models in six different dimensions:backbone network,data sets,image types,model performance, classification task types and park opening situation.In addition,this paper introduces the excellent application systems used to fight against COVID-19,and discusses the development trend of the studies in this field.

Key words: Corona Virus Disease 2019(COVID-19), deep learning, X-ray image, Computer Tomography(CT) image, detection and diagnosis model, epidemic control

摘要： 新型冠状病毒肺炎（COVID-19）具有高传染性和高致病性，严重威胁人民群众的生命安全和身体健康，快速准确地检测和诊断COVID-19对于疫情控制至关重要。目前COVID-19检测诊断方法主要包括核酸检测和基于医学影像的人工诊断，但是核酸检测耗时较长并且需要专用的测试盒，而基于医学影像的人工诊断过于依赖专业知识，分析耗时较长且难以发现隐匿病变。随着X射线图像和计算机断层扫描图像数据集的相继提出，科研人员在此基础上构建基于深度学习的COVID-19检测诊断模型，有效辅助了医学专家对COVID-19的高效诊断治疗。总结用于COVID-19检测诊断的主流影像数据集和相关评价指标，以模型任务和影像数据类型2个角度分类介绍现有基于深度学习的COVID-19检测诊断模型，从骨干网络、数据集、影像类型、性能表现、分类种类和开源情况6个维度进行比较与分析。此外，介绍用于抗击COVID-19的优秀应用系统，并探讨该领域的未来发展趋势。

关键词: 新型冠状病毒肺炎, 深度学习, X射线图像, 计算机断层扫描图像, 检测诊断模型, 疫情控制

CLC Number:

TP391.41

TANG Jiangping, ZHOU Xiaofei, HE Xin, CHU Xiaowen, LI Shifeng, CHANG Qingrui, ZHANG Jiyong. Survey of Studies of COVID-19 Diagnosis Based on Deep Learning[J]. Computer Engineering, 2021, 47(5): 1-15.

唐江平, 周晓飞, 贺鑫, 褚晓文, 李世锋, 常庆蕊, 张继勇. 基于深度学习的新型冠状病毒肺炎诊断研究综述[J]. 计算机工程, 2021, 47(5): 1-15.

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References

[1] WHO.Naming the coronavirus disease(COVID-19) and the virus that causes it[EB/OL].[2021-01-25].https://www.who.int/emerg-encies/diseases/novel-coronavirus-2019/technical-guidance.
[2] FU Bingjie,ZHANG Hao,YOU Xingpan,et al.Early COVID-19 and common pneumonia:clinical and imaging differential diagnosis[J].Journal of Chongqing Medical University,2020,45(7):998-1003.(in Chinese)付彬洁,张浩,游兴攀,等.早期新型冠状病毒肺炎与普通型肺炎的临床及影像学鉴别诊断[J].重庆医科大学学报,2020,45(7):998-1003.
[3] Johns Hopkins University.COVID-19 dashboard by the center for systems science and engineering at Johns Hopkins university[EB/OL].[2021-01-25].https://coronavirus.jhu.edu/map.html.
[4] CHAN J F,YUAN S F,KOK K,et al.A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission:a study of a family cluster[J].The Lancet,2020,395(10223):514-523.
[5] ZHU Na,ZHANG Dingyu,WANG Wenling,et al.A novel coronavirus from patients with pneumonia in China,2019[J].New England Journal of Medicine,2020,382(8):727-733.
[6] CHEN Nanshan,ZHOU Min,DONG Xuan,et al.Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan,China:a descriptive study[J].The Lancet,2020,395(10223):507-513.
[7] YANG Xiaobo,YU Yuan,XU Jiqian,et al.Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan,China:a single-centred,retrospective,observational study[J].The Lancet Respiratory Medicine,2020,8(5):475-481.
[8] WHO.Q&A on coronaviruses(COVID-19)[EB/OL].[2021-01-25].https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/q-a-coronaviruses.
[9] CHIN A,CHU J L,PERERA M,et al.Stability of SARS-CoV-2 in different environmental conditions[J].The Lancet Microbe,2020,1(1):1-5.
[10] SILVERMAN J D,HUPERT N,WASHBURNE A D.Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States[J].Science Translational Medicine,2020,12(554):1-11.
[11] WU Aiping,PENG Yousong,HUANG Baoying,et al.Genome composition and divergence of the novel coronavirus(2019-nCoV) originating in China[J].Cell Host & Microbe,2020,27(3):325-328.
[12] ZHANG Wei,HOU Wei,LI Tongzeng,et al.Clinical characteristics analysis of 74 cases of COVID-19[J].Journal of Capital Medical University,2020,41(2):161-167.(in Chinese)张维,侯维,李侗曾,等.74例新型冠状病毒肺炎临床特征分析[J].首都医科大学学报,2020,41(2):161-167.
[13] GUAN Weijie,NI Zhengyi,HU Yu,et al.Clinical characteristics of coronavirus disease 2019 in China[J].New England Journal of Medicine,2020,382(18):1708-1720.
[14] WU Z,McGOOGAN J M.Characteristics of and important lessons from the Coronavirus Disease 2019(COVID-19) outbreak in China:summary of a report of 72314 cases from the Chinese center for disease control and prevention[J].JAMA,2020,323(13):1239-1242.
[15] HOPE M D,RAPTIS C A,HENRY T S.Chest computed tomography for detection of Coronavirus Disease 2019(COVID-19):don't rush the science[J].Annals of Internal Medicine,2020,173(2):1-3
[16] LAUER S A,GRANTZ K H,BI Q F,et al.The incubation period of Coronavirus Disease 2019(COVID-19) from publicly reported confirmed cases:estimation and application[J].Annals of Internal Medicine,2020,172(9):577-582.
[17] WHO.Clinical management of severe acute respiratory infection when novel coronavirus(2019-nCoV) infection is suspected:interim guidance[EB/OL].[2021-01-25].https://apps.who.int/ir-is/handle/10665/330893.
[18] LI Shixue,SHAN Ying.Review of research progress in COVID-19[J].Journal of Shandong University(Medical Edition),2020,58(3):19-25.(in Chinese)李士雪,单莹.新型冠状病毒肺炎研究进展述评[J].山东大学学报(医学版),2020,58(3):19-25.
[19] FU Hang,XU Huanyan,ZHANG Na,et al.Association between clinical,laboratory and CT characteristics and RT-PCR results in the follow-up of COVID-19 patients[EB/OL].[2021-01-25].https://www.med-rxiv.org/content/10.1101/2020.03.19.20038315v1.full.pdf.
[20] XIE Xingzhi,ZHONG Zheng,ZHAO Wei,et al.Chest CT for typical 2019-nCoV pneumonia:relationship to negative RT-PCR testing[J].Radiology,2020,296(2):1-5.
[21] National Health Commission of the People's Republic of China.Diagnosis and treatment protocol for COVID-19(trial version7)[EB/OL].[2021-01-25].http://en.nhc.gov.cn/2020-03/29/c_78469.htm.
[22] AI Tao,YANG Zhenlu,HOU Hongyan,et al.Correlation of chest CT and RT-PCR testing in Coronavirus Disease 2019(COVID-19) in China:a report of 1014 cases[J].Radiology,2020,296(2):1-9.
[23] SHI Heshui,HAN Xiaoyu,JIANG Nanchuan,et al.Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan,China:a descriptive study[J].The Lancet Infectious Diseases,2020,20(4):425-434.
[24] CHUNG M,BERNHEIM A,MEI X Y,et al.CT imaging features of 2019 novel Coronavirus(2019-nCoV)[J].Radiology,2020,295(1):202-207.
[25] ZHONG Feiyang,ZHANG Hanfei,WANG Binchen,et al.CT imaging findings in novel coronavirus pneumonia[J].Medical Journal of Wuhan University,2020,41(3):345-348.(in Chinese)钟飞扬,张寒菲,王彬宸,等.新型冠状病毒肺炎的CT影像学表现[J].武汉大学学报(医学版),2020,41(3):345-348.
[26] YE Zheng,ZHANG Yun,WANG Yi,et al.Chest CT manifestations of new Coronavirus Disease 2019(COVID-19):a pictorial review[J].European Radiology,2020,30:4381-4389.
[27] RODRIGUEZ-MORALES A J,CARDONA-OSPINA J A,GUTIÉRREZ-OCAMPO E,et al.Clinical,laboratory and imaging features of COVID-19:a systematic review and meta-analysis[J].Travel Medicine and Infectious Disease,2020,34:1-19.
[28] ZHANG Nan,ZOU Mingyu,ZHOU Shu.Application of CT low-dose scan combined with AI-assisted diagnosis system in the examination of COVID-19[J].Chinese Medical Equipment Journal,2020,41(5):9-11.(in Chinese)张楠,邹明宇,周姝.CT低剂量扫描结合AI辅助诊断系统在新型冠状病毒肺炎检查中的应用[J].医疗卫生装备,2020,41(5):9-11.
[29] RAJPURKAR P,IRVIN J,ZHU K,et al.CheXNet:radiologist-level pneumonia detection on chest X-rays with deep learning[EB/OL].[2021-01-25].https://arxiv.org/pdf/1711.05225.pdf.
[30] ZECH J R,BADGELEY M A,LIU M,et al.Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs:a cross-sectional study[J].PLoS Medicine,2018,15(11):1-17.
[31] ZHANG Kang,LIU Xiaohong,SHEN Jun,et al.Clinically applicable AI system for accurate diagnosis,quantitative measurements and prognosis of COVID-19 pneumonia using computed tomography[J].Cell,2020,181(6):1423-1433.
[32] COHEN J P,MORRISON P,DAO L,et al.COVID-19 image data collection:prospective predictions are the future[EB/OL].[2021-01-25].https://arxiv.org/pdf/2006.11988.pdf.
[33] Kaggle.Chest X-Ray images (pneumonia)[EB/OL].[2021-01-25].https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.
[34] YANG Xingyi,HE Xuehai,ZHAO Jinyu,et al.COVID-CT-Dataset:a CT scan dataset about COVID-19[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.13865.pdf.
[35] MA Jun,GE Cheng,WANG Yixin,et al.COVID-19 CT lung and infection segmentation dataset[EB/OL].[2021-01-25].https://doi.org/10.5281/zenodo.3757476.
[36] MOROZOV S P,ANDREYCHENKO A E,PAVLOV N A,et al.MosMedData:chest CT scans with COVID-19 related findings dataset[EB/OL].[2021-01-25].https://arxiv.org/pdf/2005.06465.pdf.
[37] SOARES E,ANGELOV P,BIASO S,et al.SARS-CoV-2 CT-scan dataset:a large dataset of real patients CT scans for SARS-CoV-2 identification[EB/OL].[2021-01-25].https://www.medrxiv.org/content/10.1101/2020.04.24.20078584v3.full.pdf.
[38] RAHIMZADEH M,ATTAR A,SAKHAEI S M.A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset[EB/OL].[2021-01-25].https://www.med-rxiv.org/content/10.1101/2020.06.08.20121541v2.full.pdf.
[39] PENG Y F,TANG Y X,LEE S,et al.COVID-19-CT-CXR:a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature[EB/OL].[2021-01-25].https://arxiv.org/pdf/2006.06177.pdf.
[40] GOUTTE C,GAUSSIER E.A probabilistic interpretation of precision,recall and F-score,with implication for evaluation[C]//Proceedings of European Conference on Information Retrieval.Berlin,Germany:Springer,2005:345-359.
[41] SOKOLOVA M,JAPKOWICZ N,SZPAKOWICZ S.Beyond accuracy,F-score and ROC:a family of discriminant measures for performance evaluation[C]//Proceedings of Australasian Joint Conference on Artificial Intelligence.Washington D.C.,USA:IEEE Press,2006:1015-1021.
[42] WANG Shuai,KANG Bo,MA Jinlu,et al.A deep learning algorithm using CT images to screen for corona virus disease(COVID-19)[EB/OL].[2021-01-25].https://www.medrxiv.org/content/medrxiv/early/2020/02/17/2020.02.14.20023028.full.pdf.
[43] SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:1-9.
[44] HASTIE T,ROSSET S,ZHU J,et al.Multi-class AdaBoost[J].Statistics and Its Interface,2009,2(3):349-360.
[45] SONG Ying,ZHENG Shuangjia,LI Liang,et al.Deep learning enables accurate diagnosis of novel coronavirus(COVID-19) with CT images[EB/OL].[2021-01-25].https://www.medrxiv.org/content/10.1101/2020.02.23. 20026930v1.full.pdf.
[46] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:770-778.
[47] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:2117-2125.
[48] FU Jianlong,ZHENG Heliang,MEI Tao.Look closer to see better:recurrent attention convolutional neural network for fine-grained image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:4438-4446.
[49] WANG Xin,KONG Bin,SONG Qi,et al.Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT[J].Radiology,2020,296(2):1-17.
[50] CHEN Jun,WU Lian,ZHANG Jun,et al.Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography:a prospective study[EB/OL].[2021-01-25].https://www.medrxiv.org/content/10.1101/2020.02.25.20021568v2.full.pdf.
[51] ZHOU Z W,SIDDIQUEE M,TAJBAKHSH N,et al.UNet++:a nested U-Net architecture for medical image segmentation[M]//CARDOSO J,ARBEL T,CARNEIRO G,et al.Deep learning in medical image analysis and multimodal learning for clinical decision support.Berlin,Germany:Springer,2018:3-11.
[52] RONNEBERGER O,FISCHER P,BROX T.U-Net:convolutional networks for biomedical image segmentation[M]//OURSELIN S,JOSKOWICZ L,SABUNCU M R,et al.Medical image computing and computer-assisted intervention.Berlin,Germany:Springer,2015:234-241.
[53] JAISWAL A,GIANCHANDANI N,SINGH D,et al.Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning[EB/OL].(2020-06-03)[2021-01-25].https://www.tandfonline.com/doi/full/10.1080/07391102.2020.1788642.
[54] BUTT C,GILL J,CHUN D,et al.Deep learning system to screen Corona-Virus Disease 2019 pneumonia[EB/OL].(2020-04-22)[2021-01-25].http://link.springer.com/content/pdf/10.1007/s10489-020-01714-3.pdf.
[55] HARA K,KATAOKA H,SATOH Y.Learning spatio-temporal features with 3D residual networks for action recognition[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2017:3154-3160.
[56] GOZES O,FRID-ADAR M,GREENSPAN H,et al.Rapid AI development cycle for the coronavirus(COVID-19) pandemic:initial results for automated detection & patient monitoring using deep learning CT image analysis[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.05037.pdf.
[57] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Identity mappings in deep residual networks[EB/OL].[2021-01-25].https://arxiv.org/pdf/1603.05027.pdf.
[58] JIN Shuo,WANG Bo,XU Haibo,et al.AI-assisted CT imaging analysis for COVID-19 screening:building and deploying a medical AI system in four weeks[EB/OL].[2021-01-25].https://www.medrxiv.org/content/10.1101/2020.03.19.20039354v1.full.pdf.
[59] AMYAR A,MODZELEWSKI R,RUAN S.Multi-task deep learning based CT imaging analysis for COVID-19:classification and segmentation[EB/OL].[2021-01-25].https://www.medrxiv.org/content/10.1101/2020.04.16. 20064709v1.full.pdf.
[60] CARUANA R.Multitask learning[J].Machine Learning,1997,28(1):41-75.
[61] GONCHAROV M,PISOV M,SHEVTSOV A,et al.CT-based COVID-19 triage:deep multitask learning improves joint identification and severity quantification[EB/OL].[2021-01-25].https://arxiv.org/pdf/2006.01441.pdf.
[62] GHOSHAL B,TUCKER A.Estimating uncertainty and interpretability in deep learning for coronavirus(COVID-19) detection[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.10769.pdf.
[63] NARIN A,KAYA C,PAMUK Z.Automatic detection of coronavirus disease(COVID-19) using X-ray images and deep convolutional neural networks[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.10849.pdf.
[64] SZEGEDY C,IOFFE S,VANHOUCKE V,et al.Inception-v4,inception-ResNet and the impact of residual connections on learning[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence.San Francisco,USA:AAAI,2017:4278-4284.
[65] ZHANG Jianpeng,XIE Yutong,LIAO Zhibin,et al.Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.12338.pdf.
[66] TAN M,LE Q V.EfficientNet:rethinking model scaling for convolutional neural networks[C]//Proceedings of International Conference on Machine Learning.Long Beach,USA:IMLS,2019:6105-6114.
[67] WANG L,WONG A.COVID-Net:a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.09871.pdf.
[68] DENG J,DONG W,SOCHER R,et al.ImageNet:a large-scale hierarchical image database[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2009:248-255.
[69] APOSTOLOPOULOS I D,MPESIANA T A.COVID-19:automatic detection from X-ray images utilizing transfer learning with convolutional neural networks[J].Physical and Engineering Sciences in Medicine,2020,43(2):635-640.
[70] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2021-01-25].https://arxiv.org/pdf/1409.1556.pdf.
[71] HOWARD A G,ZHU M L,CHEN B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2021-01-25].https://arxiv.org/pdf/1704.04861.pdf.
[72] CHOLLET F.Xception:deep learning with depthwise separable convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:1251-1258.
[73] PAN S J,TSANG I W,KWOK J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210.
[74] WONG H Y F,LAM H Y S,FONG A H,et al.Frequency and distribution of chest radiographic findings in COVID-19 positive patients[J].Radiology,2020,296:72-78.
[75] ZHENG Chuansheng,DENG Xianbo,FU Qing,et al.Deep learning-based detection for COVID-19 from chest CT using weak label[EB/OL].[2021-01-25].https://www.medrxiv.org/content/medrxiv/early/2020/03/26/2020.03.12.20027185.full.pdf.
[76] WANG Shuo,ZHA Yunfei,LI Weimin,et al.A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis[J].European Respiratory Journal,2020,56(1):1-31.
[77] HUANG G,LIU Z,MAATEN L V D,et al.Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:4700-4708.
[78] WANG Shuo,SHI Jingyun,YE Zhaoxiang,et al.Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning[J].European Respiratory Journal,2019,53(3):1-45.
[79] SHI Feng,XIA Liming,SHAN Fei,et al.Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.09860.pdf.
[80] SHAN Fei,GAO Yaozong,WANG Jun,et al.Lung infection quantification of COVID-19 in CT images with deep learning[EB/OL].[2021-01-25].https://arxiv.org/pdf/2003.04655.pdf.
[81] PARK T,CASELLA G.The Bayesian lasso[J].Journal of the American Statistical Association,2008,103(482):681-686.
[82] HE Xin,WANG Shihao,SHI Shaohuai,et al.Benchmarking deep learning models and automated model design for COVID-19 detection with chest CT scans[EB/OL].[2021-01-25].https://www.med-rxiv.org/content/10.1101/2020.06.08.20125963v2.full.pdf.
[83] LI L,TALWALKAR A.Random search and reproducibility for neural architecture search[EB/OL].[2021-01-25].https://arxiv.org/pdf/1902.07638.pdf.
[84] PHAM H,GUAN M Y,ZOPH B,et al.Efficient neural architecture search via parameter sharing[EB/OL].[2021-01-25].https://arxiv.org/pdf/1802.03268.pdf.
[85] YU K C,SCIUTO C,JAGGI M,et al.Evaluating the search phase of neural architecture search[EB/OL].[2021-01-25].https://arxiv.org/pdf/1902.08142.pdf.
[86] PATHAK Y,SHUKLA P K,ARYA K V.Deep bidirectional classification model for COVID-19 disease infected patients[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2020,1:1-5.
[87] LIAO Qing,FAN Qinqin,LI Junjun.Translation control of an immersed tunnel element using a multi-objective differential evolution algorithm[J].Computers & Industrial Engineering,2019,130:158-165.
[88] CHEN Huijun,GUO Juanjuan,WANG Chen,et al.Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women:a retrospective review of medical records[J].The Lancet,2020,395(10226):809-915.
[89] HU Zhiliang,SONG Ci,XU Chuanjun,et al.Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing,China[J].Science China Life Sciences,2020,63:706-711.
[90] JIANG Fang,DENG Liehua,ZHANG Liangqing,et al.Review of the clinical characteristics of Coronavirus Disease 2019(COVID-19)[J].Journal of General Internal Medicine,2020,35:1545-1549.
[91] McCALL B.COVID-19 and artificial intelligence:protecting health-care workers and curbing the spread[J].The Lancet Digital Health,2020,2(4):166-167.
[92] BAI Li,YANG Dawei,WANG Xun,et al.Chinese experts' consensus on the Internet of Things-aided diagnosis and treatment of Coronavirus Disease 2019(COVID-19)[J].Clinical eHealth,2020,3:7-15.
[93] OBERMEYER Z,EMANUEL E J.Predicting the future-big data,machine learning,and clinical medicine[J].The New England Journal of Medicine,2016,375(13):1216-1219.
[94] WANG C J,NG C Y,BROOK R H.Response to COVID-19 in Taiwan:big data analytics,new technology,and proactive testing[J].JAMA,2020,323(14):1341-1342.
[95] SINGH D,KUMAR V,KAUR M.Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks[J].European Journal of Clinical Microbiology & Infectious Diseases,2020,39(7):1-11.
[96] MAGHDID H S,ASAAD A T,GHAFOOR K Z,et al.Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms[EB/OL].[2021-01-25].https://arxiv.org/pdf/2004.00038.pdf.
[97] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3431-3440.
[98] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:580-587.
[99] DOERSCH C,ZISSERMAN A.Multi-task self-supervised visual learning[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2017:2051-2060.
[100] PATHAK D,AGRAWAL P,EFROS A A,et al.Curiosity-driven exploration by self-supervised prediction[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:16-17.
[101] YANG Ran,LI Xiang,LIU Huan,et al.Chest CT severity score:an imaging tool for assessing severe COVID-19[J].Radiology:Cardiothoracic Imaging,2020,2(2):1-8.
[102] SHI Weiya,PENG Xueqing,LIU Tiefu,et al.Deep learning-based quantitative computed tomography model in predicting the severity of COVID-19:a retrospective study in 196 patients[EB/OL].[2021-01-02].https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3546089.
[103] ZHANG Wenlu,LI Rongjian,DENG Houtao,et al.Deep convolutional neural networks for multi-modality isointense infant brain image segmentation[J].NeuroImage,2015,108:214-224.
[104] HUANG Y W,SHAO L,FRANGI A F.Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:6070-6079.
[105] ROTMENSCH M,HALPERN Y,TLIMAT A,et al.Learning a health knowledge graph from electronic medical records[J].Scientific Reports,2017,7(1):1-11.
[106] HE Xin,ZHAO Kaiyong,CHU Xiaowen.AutoML:a survey of the state-of-the-art[EB/OL].[2021-01-25].https://arxiv.org/pdf/1908.00709.pdf.
[107] FAES L,WAGNER S K,FU D J,et al.Automated deep learning design for medical image classification by health-care professionals with no coding experience:a feasibility study[J].The Lancet Digital Health,2019,1(5):232-242.

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