{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:20:29Z","timestamp":1740133229476,"version":"3.37.3"},"reference-count":82,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-009"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-001"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2016-04988"],"award-info":[{"award-number":["RGPIN-2016-04988"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Signal Process. Mag."],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1109\/msp.2021.3090674","type":"journal-article","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T20:22:41Z","timestamp":1630095761000},"page":"37-66","source":"Crossref","is-referenced-by-count":15,"title":["Diagnosis\/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications"],"prefix":"10.1109","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1972-7923","authenticated-orcid":false,"given":"Arash","family":"Mohammadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0445-3632","authenticated-orcid":false,"given":"Yingxu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nastaran","family":"Enshaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8440-9836","authenticated-orcid":false,"given":"Parnian","family":"Afshar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farnoosh","family":"Naderkhani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anastasia","family":"Oikonomou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javad","family":"Rafiee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helder","family":"Rodrigues de Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4794-9849","authenticated-orcid":false,"given":"Svetlana","family":"Yanushkevich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3647-5473","authenticated-orcid":false,"given":"Konstantinos N.","family":"Plataniotis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000689"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2994762"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3018181"},{"key":"ref70","article-title":"Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays","author":"das","year":"2020","journal-title":"IRBM"},{"journal-title":"Performance and robustness of machine learning-based radiomic COVID-19 severity prediction","year":"2020","author":"yip","key":"ref76"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2020.09.004"},{"journal-title":"Severity assessment and progression prediction of COVID-19 patients based on the lesion encoder framework and chest CT","year":"2020","author":"feng","key":"ref74"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101844"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0236621"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107323"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200079"},{"journal-title":"Transunet Transformers make strong encoders for medical image segmentation","year":"2021","author":"chen","key":"ref79"},{"journal-title":"Lung infection quantification of COVID-19 in CT images with deep learning","year":"2020","author":"shan","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108109"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3027738"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.04.045"},{"key":"ref37","first-page":"65031","volume":"66","author":"shi","year":"2020","journal-title":"Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification"},{"journal-title":"Label-free segmentation of COVID-19 lesions in Lung CT","year":"2020","author":"yao","key":"ref36"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3000314"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07042-x"},{"journal-title":"Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images","year":"2020","author":"ying","key":"ref60"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005510"},{"journal-title":"Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection","year":"2020","author":"zhang","key":"ref61"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100412"},{"journal-title":"Miniseg An extremely minimum network for efficient COVID-19 segmentation","year":"2020","author":"qiu","key":"ref28"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995965"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3018498"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-021-00146-8"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2020.03.132"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3034296"},{"journal-title":"Coronavirus detection and analysis on chest CT with deep learning","year":"2020","author":"gozes","key":"ref69"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2900993"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7326\/M20-1495"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201754"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-020-01232-9"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1111\/echo.14664"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021203957"},{"key":"ref26","article-title":"Dynamic deformable attention (DDANET) for semantic segmentation","author":"rajamani","year":"2020","journal-title":"medRxiv"},{"journal-title":"Guidance on COVID-19 and MR use","article-title":".","year":"0","key":"ref25"},{"journal-title":"A quantitative lung computed tomography image feature for multi-center severity assessment of COVID-19","year":"2020","author":"ghosh","key":"ref50"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0931-3"},{"journal-title":"COVIDX-Net A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images","year":"2020","author":"hemdan","key":"ref59"},{"journal-title":"Deep COVID explainer Explainable COVID-19 predictions based on chest X-ray images","year":"2020","author":"karim","key":"ref58"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"journal-title":"Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks","year":"2020","author":"narin","key":"ref56"},{"journal-title":"COVID-ResNet A Deep Learning Framework for Screening of COVID19 from Radiographs","year":"2020","author":"farooq","key":"ref55"},{"journal-title":"A Deep Learning Algorithm Using CT Images to Screen for Corona Virus Disease (COVID-19)","year":"2020","author":"wang","key":"ref54"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.09.010"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.04.003"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1136\/emermed-2020-210125"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00633-5"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.21037\/qims-20-564"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-021-00900-3"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200463"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200823"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.21227\/sed8-6r15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200075"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69106-8"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200048"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07033-y"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"72e","DOI":"10.1148\/radiol.2020201160","article-title":"Frequency and distribution of chest radiographic findings in COVID-19 positive patients","volume":"296","author":"]","year":"2020","journal-title":"Radiology"},{"journal-title":"UNETR Transformers for 3D medical image segmentation","year":"2021","author":"hatamizadeh","key":"ref80"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2990959"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2987975"},{"key":"ref6","article-title":"The cognitive and mathematical foundations of analytic epidemiology","author":"wang","year":"0","journal-title":"Proc IEEE 19th Int Conf Cogn Informatics Cogn Comput (ICCI*CC’20)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3001973"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IPCC.2011.6087229"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0362"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abbf9e"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.4018\/jssci.2009062501"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101860"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200322"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037"},{"journal-title":"Predicting COVID-19 malignant progression with AI techniques","year":"2020","author":"bai","key":"ref47"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201433"},{"key":"ref41","first-page":"hal-02586111v3","article-title":"AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients","author":"lassau","year":"2020","journal-title":"Inria Saclay Ile de France Res Rep"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.21037\/atm-20-3026"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejro.2020.100272"}],"container-title":["IEEE Signal Processing Magazine"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/79\/9524538\/09524588.pdf?arnumber=9524588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:51:11Z","timestamp":1652194271000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9524588\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":82,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/msp.2021.3090674","relation":{},"ISSN":["1053-5888","1558-0792"],"issn-type":[{"type":"print","value":"1053-5888"},{"type":"electronic","value":"1558-0792"}],"subject":[],"published":{"date-parts":[[2021,9]]}}}