{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:35:49Z","timestamp":1769207749984,"version":"3.49.0"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministry of Economy and Competitiveness of Spain","doi-asserted-by":"publisher","award":["TIN2016-75097-P"],"award-info":[{"award-number":["TIN2016-75097-P"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministry of Economy and Competitiveness of Spain","doi-asserted-by":"publisher","award":["PPIT.UMA.B1.2017"],"award-info":[{"award-number":["PPIT.UMA.B1.2017"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Ministry of Science, Innovation and Universities of Spain through the Project \u201cAutomated Detection With Low Cost Hardware of Unusual Activities in Video Sequences\u201d","doi-asserted-by":"publisher","award":["RTI2018-094645-B-I00"],"award-info":[{"award-number":["RTI2018-094645-B-I00"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011011","name":"Autonomous Government of Andalusia, Spain, through the Project \u201cDetection of Anomalous Behavior Agents by Deep Learning in Low Cost Video Surveillance Intelligent Systems\u201d","doi-asserted-by":"publisher","award":["UMA18-FEDERJA-084"],"award-info":[{"award-number":["UMA18-FEDERJA-084"]}],"id":[{"id":"10.13039\/501100011011","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009473","name":"Universidad de M\u00e1laga and the Instituto de Investigaci\u00f3n Biom\u00e9dica de M\u00e1laga - IBIMA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009473","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3085418","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T03:43:25Z","timestamp":1622691805000},"page":"85442-85454","source":"Crossref","is-referenced-by-count":38,"title":["Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-4388","authenticated-orcid":false,"given":"Saul","family":"Calderon-Ramirez","sequence":"first","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7222-4917","authenticated-orcid":false,"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester, U.K."}]},{"given":"Armaghan","family":"Moemeni","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Nottingham, Nottingham, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1795-6995","authenticated-orcid":false,"given":"Simon","family":"Colreavy-Donnelly","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester, U.K."}]},{"given":"David A.","family":"Elizondo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester, U.K."}]},{"given":"Luis","family":"Oala","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Department, XAI Group, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-6264","authenticated-orcid":false,"given":"Jorge","family":"Rodriguez-Capitan","sequence":"additional","affiliation":[{"name":"CIBERCV, Hospital Universitario Virgen de la Victoria, Málaga, Spain"}]},{"given":"Manuel","family":"Jimenez-Navarro","sequence":"additional","affiliation":[{"name":"CIBERCV, Hospital Universitario Virgen de la Victoria, Málaga, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8231-5687","authenticated-orcid":false,"given":"Ezequiel","family":"Lopez-Rubio","sequence":"additional","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of Málaga, Málaga, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8929-6017","authenticated-orcid":false,"given":"Miguel A.","family":"Molina-Cabello","sequence":"additional","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of Málaga, Málaga, Spain"}]}],"member":"263","reference":[{"key":"ref39","article-title":"AugMix: A simple data processing method to improve robustness and uncertainty","author":"hendrycks","year":"2020","journal-title":"arXiv 1912 02781"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1175\/WAF993.1"},{"key":"ref33","first-page":"1392","article-title":"Unlabelled data improves Bayesian uncertainty calibration under covariate shift","author":"chan","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref32","article-title":"Mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"arXiv 1710 09412"},{"key":"ref31","first-page":"5050","article-title":"Mixmatch: A holistic approach to semi-supervised learning","author":"berthelot","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","article-title":"Objective evaluation of deep uncertainty predictions for COVID-19 detection","author":"asgharnezhad","year":"2020","journal-title":"arXiv 2012 11840"},{"key":"ref37","first-page":"38","article-title":"Measuring calibration in deep learning","author":"nixon","year":"2019","journal-title":"Proc CVPR Workshops"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.2172\/1525811"},{"key":"ref35","first-page":"11192","article-title":"Unlabeled data improves adversarial robustness","author":"carmon","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref34","first-page":"15663","article-title":"Using self-supervised learning can improve model robustness and uncertainty","author":"hendrycks","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","article-title":"COVID-19 image data collection: Prospective predictions are the future","author":"paul cohen","year":"2020","journal-title":"arXiv 2006 11988"},{"key":"ref27","article-title":"Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection","author":"ghoshal","year":"2020","journal-title":"arXiv 2003 10769"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1261\/rna.076232.120"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molmed.2020.02.008"},{"key":"ref20","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref22","article-title":"Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network","author":"van amersfoort","year":"2020","journal-title":"arXiv 2003 02037"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569637"},{"key":"ref24","article-title":"Interval neural networks: Uncertainty scores","author":"oala","year":"2020","journal-title":"arXiv 2003 11566"},{"key":"ref23","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2919951"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-17876-z"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-41005-6_21"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00888-x"},{"key":"ref11","first-page":"5294","article-title":"Dealing with scarce labelled data: Semi-supervised deep learning with mix match for COVID-19 detection using chest X-ray images","author":"calderon-ramirez","year":"2021","journal-title":"Proc Int Conf Pattern Recognit (ICPR)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759581"},{"key":"ref12","article-title":"Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images","author":"calderon-ramirez","year":"2020","journal-title":"arXiv 2008 08496"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1312"},{"key":"ref14","first-page":"280","article-title":"Ml4h auditing: From paper to practice","author":"oala","year":"2020","journal-title":"Proc Mach Learn Health (PMLR)"},{"key":"ref15","article-title":"Explaining deep neural networks and beyond: A review of methods and applications","author":"samek","year":"2020","journal-title":"arXiv 2003 07631"},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1007\/s10479-021-04006-2","article-title":"Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)","author":"alizadehsani","year":"2021","journal-title":"Ann Oper Res"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.strusafe.2008.06.020"},{"key":"ref18","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2016","journal-title":"arXiv 1610 02136"},{"key":"ref19","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30211-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1128\/JCM.00310-20"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200274"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200230"},{"key":"ref8","first-page":"449","article-title":"The training and practice of radiology in India: Current trends","volume":"4","author":"arora","year":"2014","journal-title":"Quant Imaging Med Surg"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00268-014-2805-7"},{"key":"ref49","article-title":"MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures","author":"calderon-ramirez","year":"2020","journal-title":"arXiv 2006 07767"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105608"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2018.8517589"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00865-4"},{"key":"ref48","article-title":"More than meets the eye: Semi-supervised learning under non-IID data","author":"calderon-ramirez","year":"2021","journal-title":"arXiv 2104 10223"},{"key":"ref47","article-title":"A disciplined approach to neural network hyper-parameters: Part 1—Learning rate, batch size, momentum, and weight decay","author":"smith","year":"2018","journal-title":"arXiv 1803 09820"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-4371(03)00566-1"},{"key":"ref41","first-page":"7047","article-title":"Predictive uncertainty estimation via prior networks","author":"malinin","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref44","article-title":"COVIDX-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images","author":"el-d hemdan","year":"2020","journal-title":"arXiv 2003 11055"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09445026.pdf?arnumber=9445026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T20:44:28Z","timestamp":1642193068000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9445026\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3085418","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}