{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:31:06Z","timestamp":1775799066895,"version":"3.50.1"},"reference-count":75,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-009"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-001"}],"funder":[{"name":"Amazon Faculty Research Fellow"},{"name":"Microsoft Azure Cloud"},{"name":"Petit Institute Faculty"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1109\/jbhi.2021.3074893","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T19:58:41Z","timestamp":1619035121000},"page":"2376-2387","source":"Crossref","is-referenced-by-count":76,"title":["COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8972-7342","authenticated-orcid":false,"given":"Wenqi","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1556-1241","authenticated-orcid":false,"given":"Li","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7812-9216","authenticated-orcid":false,"given":"Yuanda","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-3608","authenticated-orcid":false,"given":"May D.","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref73","article-title":"What do you see? evaluation of explainable artificial intelligence (XAI) interpretability through neural backdoors","author":"lin","year":"0"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz089"},{"key":"ref71","article-title":"Lung segmentation from chest X-rays using variational data imputation","author":"selvan","year":"0","journal-title":"ICML Workshop Art Learn Missing Values"},{"key":"ref70","article-title":"COVIDNet-CT: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images","author":"gunraj","year":"0"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.76.10.5269"},{"key":"ref39","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-74399-w"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.232"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_42"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00960"},{"key":"ref37","first-page":"3146","article-title":"Dual attention network for scene segmentation","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref36","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"woo","year":"2018","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00117"},{"key":"ref34","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/18.61115"},{"key":"ref62","article-title":"Temporal ensembling for semi-supervised learning","author":"laine","year":"0"},{"key":"ref61","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0"},{"key":"ref63","article-title":"COVID-CT-dataset: A CT scan dataset about COVID-19","author":"zhao","year":"0"},{"key":"ref28","article-title":"Hypergraph learning for identification of COVID-19 with CT imaging","author":"di","year":"0"},{"key":"ref64","article-title":"Radiopaedia pneumonia dataset","author":"knipe","year":"0"},{"key":"ref27","article-title":"Lung infection quantification of COVID-19 in CT images with deep learning","author":"shan","year":"0"},{"key":"ref65","article-title":"COVID-19 database","year":"0"},{"key":"ref66","article-title":"COVID-19 image data collection: Prospective predictions are the future","author":"cohen","year":"0"},{"key":"ref29","article-title":"Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection","author":"ghoshal","year":"0"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105581"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.3786"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30185-9"},{"key":"ref20","article-title":"DeepCOVIDExplainer: Explainable COVID-19 predictions based on chest X-ray images","author":"karim","year":"0"},{"key":"ref22","article-title":"SODA: Detecting COVID-19 in chest X-rays with semi-supervised open set domain adaptation","author":"zhou","year":"0"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"ref24","article-title":"Predicting COVID-19 pneumonia severity on chest X-ray with deep learning","author":"cohen","year":"0"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06801-0"},{"key":"ref26","article-title":"Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification","author":"shi","year":"0"},{"key":"ref25","article-title":"COVID-19 in CXR: From detection and severity scoring to patient disease monitoring","author":"amer","year":"0"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00552"},{"key":"ref58","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"0"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref55","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"zagoruyko","year":"0"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995508"},{"key":"ref53","article-title":"JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation","author":"wu","year":"0"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00865-4"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_24"},{"key":"ref12","article-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images","author":"song","year":"0","journal-title":"medRxiv"},{"key":"ref13","article-title":"Advancing medical imaging informatics by deep learning-based domain adaptation","volume":"29","author":"choudhary","year":"2020","journal-title":"Yearbook Med Inform"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002315"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3107411.3107445"},{"key":"ref16","article-title":"What do we need to build explainable ai systems for the medical domain","author":"holzinger","year":"2017"},{"key":"ref17","article-title":"Improve model generalization and robustness to dataset bias with bias-regularized learning and domain-guided augmentation","author":"zhang","year":"0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2382936.2382964"},{"key":"ref19","article-title":"COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images","author":"wang","year":"0"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases","author":"ai","year":"2020","journal-title":"Radiology"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200432"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m2426"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200823"},{"key":"ref8","article-title":"Sample-efficient deep learning for COVID-19 diagnosis based on CT scans","author":"he","year":"2020","journal-title":"medRxiv"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2987975"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref9","article-title":"COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images","author":"hemdan","year":"0"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1700808"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref47","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"simonyan","year":"0"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3388440.3412455"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_29"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2950006"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2952597"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9497060\/09410346.pdf?arnumber=9410346","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:00Z","timestamp":1652194380000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9410346\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":75,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2021.3074893","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}