{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:47:27Z","timestamp":1778586447235,"version":"3.51.4"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"EPSRC DTP Studentship","award":["#2110275"],"award-info":[{"award-number":["#2110275"]}]},{"name":"EPSRC DTP Studentship","award":["EP\/R513271\/1"],"award-info":[{"award-number":["EP\/R513271\/1"]}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R511729\/1"],"award-info":[{"award-number":["EP\/R511729\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1109\/jbhi.2020.3012383","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T21:48:55Z","timestamp":1595972935000},"page":"2776-2786","source":"Crossref","is-referenced-by-count":42,"title":["Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models"],"prefix":"10.1109","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8798-4134","authenticated-orcid":false,"given":"Joshua","family":"Bridge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7344-2174","authenticated-orcid":false,"given":"Yanda","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4357-4592","authenticated-orcid":false,"given":"Yitian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingfeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renrong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.7326\/M18-1376"},{"key":"ref38","article-title":"Deep learning-based detection for COVID-19 from chest CT using weak label","author":"zheng","year":"2020","journal-title":"medRxiv"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref32","article-title":"Semantic image segmentation with deep convolutional nets and fully connected crfs","author":"chen","year":"2014"},{"key":"ref31","first-page":"2020.03.20.20039834","article-title":"Development and evaluation of an AI system for COVID-19 diagnosis","author":"jin","year":"2020","journal-title":"medRxiv"},{"key":"ref30","first-page":"2020.03.19.20039354","article-title":"AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical ai system in four weeks","author":"jin","year":"2020","journal-title":"medRxiv"},{"key":"ref37","article-title":"Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images","author":"tang","year":"2020"},{"key":"ref36","article-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images","author":"song","year":"2020","journal-title":"medRxiv"},{"key":"ref35","article-title":"A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)","author":"wang","year":"2020","journal-title":"medRxiv"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.7326\/M14-0698"},{"key":"ref28","article-title":"Coronavirus (COVID-19) classification using CT images by machine learning methods","author":"barstugan","year":"2020"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"},{"key":"ref29","article-title":"Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification","author":"shi","year":"2020"},{"key":"ref2","first-page":"200432","article-title":"Sensitivity of chest CT for COVID-19: Comparison to RT-PCR","author":"fang","year":"0","journal-title":"Radiology"},{"key":"ref1","article-title":"Coronavirus disease (COVID-2019) situation reports","year":"2020"},{"key":"ref20","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"0","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref22","article-title":"COVID-19 screening on chest X-ray images using deep learning based anomaly detection","author":"zhang","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref23","first-page":"2020.02.25.20021568","article-title":"Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study","author":"chen","year":"2020","journal-title":"medRxiv"},{"key":"ref26","article-title":"Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis","author":"gozes","year":"2020"},{"key":"ref25","article-title":"Lung infection quantification of COVID-19 in CT images with deep learning","author":"shan","year":"2020"},{"key":"ref50","first-page":"475","article-title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"jaeger","year":"2014","journal-title":"Quant Imaging Med Surg"},{"key":"ref51","article-title":"Covid-ct-dataset: A CT scan dataset about COVID-19","author":"zhao","year":"2020"},{"key":"ref59","first-page":"201160","article-title":"Frequency and distribution of chest radiographic findings in COVID-19 positive patients","author":"wong","year":"0","journal-title":"Radiology"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"ref57","author":"kundu","year":"2020","journal-title":"PredictABEL Assessment Risk Prediction Models"},{"key":"ref56","author":"du","year":"2019","journal-title":"reportROC An Easy Way to Rep ROC Anal"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: an open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"robin","year":"2011","journal-title":"BMC Bioinf"},{"key":"ref54","year":"2020","journal-title":"R A Language and Environment for Statistical Computing"},{"key":"ref53","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"abadi","year":"2015"},{"key":"ref52","article-title":"Keras","author":"chollet","year":"2015"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1142\/p191"},{"key":"ref11","author":"zhou","year":"2017","journal-title":"Deep learning for medical image analysis"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.100"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-76550-z","article-title":"COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images","author":"wang","year":"2020"},{"key":"ref13","article-title":"COVID-19 image data collection","author":"cohen","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref16","article-title":"Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection","author":"ghoshal","year":"2020"},{"key":"ref17","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","author":"narin","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200642"},{"key":"ref3","article-title":"Use of chest imaging in COVID-19","year":"2020"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2020.02.008"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m1328"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2987975"},{"key":"ref49","article-title":"Societ&#x00E1; italiana di radiologia medica e interventistica","year":"0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2936244"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2013.11.007"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/67.3.723"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2818348"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOAS354"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(92)90069-5"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9216186\/09151288.pdf?arnumber=9151288","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T22:34:16Z","timestamp":1667601256000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9151288\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10]]},"references-count":60,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2020.3012383","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10]]}}}