{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:14:04Z","timestamp":1770290044266,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10278-021-00431-8","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T18:03:19Z","timestamp":1614276199000},"page":"231-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning"],"prefix":"10.1007","volume":"34","author":[{"given":"Hongtao","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuanshuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0695-1566","authenticated-orcid":false,"given":"Yanbin","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxiong","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaolin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"431_CR1","unstructured":"Novel Coronavirus (COVID-19) Situation. World Health Organization. Available at https:\/\/experience.arcgis.com\/experience\/685d0ace521648f8a5beeeee1b9125cd. Accessed 3 April 2020."},{"key":"431_CR2","unstructured":"Situation report-11. World Health Organization. Available at https:\/\/www.who.int\/docs\/default-source\/coronaviruse\/situation-reports\/20200131-sitrep-11-ncov.pdf?sfvrsn=de7c0f7_4. Accessed 31 Jan 2020."},{"key":"431_CR3","doi-asserted-by":"crossref","unstructured":"Chung, M; Bernheim, A; Mei, X; et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). J Radiology 200230,2020","DOI":"10.1148\/radiol.2020200230"},{"key":"431_CR4","doi-asserted-by":"crossref","unstructured":"Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Du B, et al: Clinical characteristics of 2019 novel coronavirus infection in China.\u00a0medRxiv, 2020","DOI":"10.1101\/2020.02.06.20020974"},{"issue":"10223","key":"431_CR5","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang, C; Wang, Y; Li, X; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223):497-506,2020","journal-title":"The Lancet"},{"key":"431_CR6","unstructured":"\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u611f\u67d3\u7684\u80ba\u708e\u8bca\u7597\u65b9\u6848 (\u8bd5\u884c\u7b2c\u4e94\u7248) . Available at http:\/\/www.nhc.gov.cn\/yzygj\/s7652m\/202002\/e84bd30142ab4d8982326326e4db22ea.shtml. Accessed 5 Feb 2020."},{"key":"431_CR7","doi-asserted-by":"crossref","unstructured":"Cicero M, Bilbily A, Colak E, et al: Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs J Investig Radiol 52,2017","DOI":"10.1097\/RLI.0000000000000341"},{"key":"431_CR8","doi-asserted-by":"crossref","unstructured":"Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. J Radiology 162326,2017","DOI":"10.1148\/radiol.2017162326"},{"issue":"1","key":"431_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11604-018-0795-3","volume":"37","author":"D Ueda","year":"2019","unstructured":"Ueda, D; Shimazaki, A; Miki, Y; Technical and clinical overview of deep learning in radiology. Jpn J Radiol 37(1):15-33,2019","journal-title":"Jpn J Radiol."},{"issue":"2","key":"431_CR10","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1002\/mp.13367","volume":"46","author":"Y Yuan","year":"2019","unstructured":"Yuan, Y; Qin, W; Buyyounouski, M; et al. Prostate cancer classification with multiparametric MRI transfer learning model. J Med Phys 46(2):756-765,2019","journal-title":"J. Med Phys."},{"issue":"6","key":"431_CR11","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1007\/s10278-019-00204-4","volume":"32","author":"S Zhang","year":"2019","unstructured":"Zhang S, Sun F, Wang N, et al: Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning. J Digit Imaging 32(6):995-1007,2019","journal-title":"J Digit Imaging"},{"key":"431_CR12","doi-asserted-by":"publisher","first-page":"1800808","DOI":"10.1109\/JTEHM.2018.2865787","volume":"6","author":"T Tan","year":"2018","unstructured":"Tan T, Li Z, Liu H, et al: Optimize Transfer Learning for Lung Diseases in Bronchoscopy Using a New Concept: Sequential Fine-Tuning. IEEE J Transl Eng Health Med (6):1800808,2018","journal-title":"IEEE J Transl Eng Health Med"},{"issue":"11","key":"431_CR13","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1158\/1078-0432.CCR-18-2495","volume":"25","author":"Y Xu","year":"2019","unstructured":"Xu Y, Hosny A, Zeleznik R, et al: Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. J Clin Cancer Res 25(11):3266-3275,2019","journal-title":"Clin Cancer Res"},{"issue":"9","key":"431_CR14","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1038\/s41592-018-0108-x","volume":"15","author":"G Caravagna","year":"2018","unstructured":"Caravagna G, Giarratano Y, Ramazzotti D, et al: Detecting repeated cancer evolution from multi-region tumor sequencing data. J Nat Methods 15(9):707-714,2018","journal-title":"J Nat Methods"},{"issue":"5","key":"431_CR15","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/JBHI.2017.2769800","volume":"22","author":"V Cheplygina","year":"2018","unstructured":"Cheplygina V, Pena IP, Pedersen JH, et al: Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform 22(5):1486-1496,2018","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"431_CR16","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/JBHI.2016.2636929","volume":"21","author":"S Christodoulidis","year":"2017","unstructured":"Christodoulidis S, Anthimopoulos M, Ebner L, et al: Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis. IEEE J Biomed Health Inform 21(1):76-84,2017","journal-title":"IEEE J Biomed Health Inform"},{"key":"431_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al: Deep Residual Learning for Image Recognition[C]\/\/ 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"431_CR18","unstructured":"Scikit-image Documentation. scikit-image. Available at https:\/\/scikit-image.org\/docs\/dev\/index.html#. Accessed 9 February 2020."},{"key":"431_CR19","unstructured":"Krizhevsky A, Sutskever I, Hinton G. ImageNet Classification with deep convolutional neural networks[C]\/\/ NIPS. Curran Associates Inc. 2012."},{"key":"431_CR20","unstructured":"Simonyan K, Zisserman A. Very Deep Convolutional networks for large-scale image recognition. J Comp Sci 2014"},{"key":"431_CR21","doi-asserted-by":"crossref","unstructured":"Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015","DOI":"10.1007\/s11263-015-0816-y"},{"issue":"6","key":"431_CR22","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1109\/TNNLS.2018.2873722","volume":"30","author":"L Yang","year":"2019","unstructured":"Yang L, Song Q, Wu Y, et al: Attention Inspiring Receptive-Fields Network for Learning Invariant Representations. J IEEE Trans Neural Netw Learn Syst 30(6):1744-1755,2019","journal-title":"J IEEE Trans Neural Netw Learn Syst"},{"key":"431_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.ajo.2019.08.004","volume":"208","author":"BY Xu","year":"2019","unstructured":"Xu BY, Chiang M, Chaudhary S, et al: Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images. Am J Ophthalmol 208:273-280,2019","journal-title":"Am J Ophthalmol"},{"issue":"1","key":"431_CR24","doi-asserted-by":"publisher","first-page":"6381","DOI":"10.1038\/s41598-019-42294-8","volume":"9","author":"IM Baltruschat","year":"2019","unstructured":"Baltruschat IM, Nickisch H, Grass M, et al: Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification. J Sci Rep 9(1):6381,2019","journal-title":"J Sci Rep"},{"issue":"12","key":"431_CR25","doi-asserted-by":"publisher","first-page":"6057","DOI":"10.1364\/BOE.10.006057","volume":"10","author":"J Wang","year":"2019","unstructured":"Wang J, Deng G, Li W, et al: Deep learning for quality assessment of retinal OCT images. J Biomed Opt Express 10(12):6057-6072,2019","journal-title":"J Biomed Opt Express"},{"key":"431_CR26","doi-asserted-by":"publisher","first-page":"101743","DOI":"10.1016\/j.artmed.2019.101743","volume":"101","author":"M Talo","year":"2019","unstructured":"Talo M: Automated classification of histopathology images using transfer learning. J Artif Intell Med 101:101743,2019","journal-title":"J Artif Intell Med"},{"issue":"11","key":"431_CR27","doi-asserted-by":"publisher","first-page":"3790","DOI":"10.1007\/s00464-019-06677-2","volume":"33","author":"JH Lee","year":"2019","unstructured":"Lee JH, Kim YJ, Kim YW, et al: Spotting malignancies from gastric endoscopic images using deep learning. J Surg Endosc 33(11):3790-3797,2019","journal-title":"J Surg Endosc"},{"key":"431_CR28","doi-asserted-by":"crossref","unstructured":"Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, et al: Sensitivity of chest CT for COVID-19 Comparison to RT-PCR. Radiology 200432,2020","DOI":"10.1148\/radiol.2020200432"},{"key":"431_CR29","doi-asserted-by":"crossref","unstructured":"Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology 200642,2020","DOI":"10.1148\/radiol.2020200642"},{"key":"431_CR30","doi-asserted-by":"crossref","unstructured":"Li Meng: \"Chest CT features and their role in COVID-19. Radiology of infectious diseases 2020","DOI":"10.1016\/j.jrid.2020.04.001"},{"key":"431_CR31","doi-asserted-by":"crossref","unstructured":"Hope Michael D, et al: \"A role for CT in COVID-19? What data really tell us so far. http:\/\/www.thelancet.com\/article\/S0140673620307285\/pdf2020","DOI":"10.1016\/S0140-6736(20)30728-5"},{"key":"431_CR32","doi-asserted-by":"crossref","unstructured":"Simpson S, et al: Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. Radiology: Cardiothoracic Imaging 2.2 e200152,2020","DOI":"10.1148\/ryct.2020200152"},{"key":"431_CR33","doi-asserted-by":"crossref","unstructured":"Wang S, Zha Y, Li W, et al: A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 2020","DOI":"10.1101\/2020.03.24.20042317"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00431-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00431-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00431-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T14:11:33Z","timestamp":1626703893000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00431-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,25]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["431"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00431-8","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,25]]},"assertion":[{"value":"3 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research study was conducted retrospectively from data obtained for clinical purposes. We consulted extensively with the IRB of the Fifth Affiliated Hospital of Sun Yat-sen University who determined that our study did not need ethical approval. An IRB official waiver of ethical approval was granted from the IRB of the Fifth Affiliated Hospital of Sun Yat-sen University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"It is not necessary to obtain consent because of CT images without identifying information in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"The authors declare that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}