{"id":"https://openalex.org/W3121468755","doi":"https://doi.org/10.1109/bibm49941.2020.9313466","title":"Enhancing Automated COVID-19 Chest X-ray Diagnosis by Image-to-Image GAN Translation","display_name":"Enhancing Automated COVID-19 Chest X-ray Diagnosis by Image-to-Image GAN Translation","publication_year":2020,"publication_date":"2020-12-16","ids":{"openalex":"https://openalex.org/W3121468755","doi":"https://doi.org/10.1109/bibm49941.2020.9313466","mag":"3121468755"},"language":"en","primary_location":{"id":"doi:10.1109/bibm49941.2020.9313466","is_oa":true,"landing_page_url":"https://doi.org/10.1109/bibm49941.2020.9313466","pdf_url":"https://ieeexplore.ieee.org/ielx7/9312958/9312977/09313466.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/9312958/9312977/09313466.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004135809","display_name":"Zhaohui Liang","orcid":"https://orcid.org/0000-0002-9361-5535"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Zhaohui Liang","raw_affiliation_strings":["Dept.of Electrical Engineering & Computer Science, York University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Dept.of Electrical Engineering & Computer Science, York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000409439","display_name":"Jimmy Xiangji Huang","orcid":"https://orcid.org/0000-0003-1292-1491"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jimmy Xiangji Huang","raw_affiliation_strings":["School of Information Technology, York University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100361662","display_name":"Jun Li","orcid":"https://orcid.org/0000-0001-7200-5909"},"institutions":[{"id":"https://openalex.org/I117532281","display_name":"Guangzhou University of Chinese Medicine","ror":"https://ror.org/03qb7bg95","country_code":"CN","type":"education","lineage":["https://openalex.org/I117532281"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["Guangdong Provincal Hospital of Chinese Medicine, Guangzhou Univ Chinese Med, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincal Hospital of Chinese Medicine, Guangzhou Univ Chinese Med, Guangzhou, China","institution_ids":["https://openalex.org/I117532281"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102385422","display_name":"Stephen Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stephen Chan","raw_affiliation_strings":["Dapasoft INC., Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Dapasoft INC., Toronto, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004135809"],"corresponding_institution_ids":["https://openalex.org/I192455969"],"apc_list":null,"apc_paid":null,"fwci":1.9824,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.89435772,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1068","last_page":"1071"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9763000011444092,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9635999798774719,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6727002859115601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6131612062454224},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.5260106325149536},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.5129672884941101},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.4996623992919922},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4857085645198822},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4786456823348999},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.45584836602211},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42026287317276},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37799081206321716},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2831723690032959},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.18112099170684814},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13174745440483093},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10756316781044006},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.09126600623130798}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6727002859115601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6131612062454224},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5260106325149536},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.5129672884941101},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.4996623992919922},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4857085645198822},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4786456823348999},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45584836602211},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42026287317276},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37799081206321716},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2831723690032959},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.18112099170684814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13174745440483093},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10756316781044006},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.09126600623130798},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C105580179","wikidata":"https://www.wikidata.org/wiki/Q188928","display_name":"Messenger RNA","level":3,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm49941.2020.9313466","is_oa":true,"landing_page_url":"https://doi.org/10.1109/bibm49941.2020.9313466","pdf_url":"https://ieeexplore.ieee.org/ielx7/9312958/9312977/09313466.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/bibm49941.2020.9313466","is_oa":true,"landing_page_url":"https://doi.org/10.1109/bibm49941.2020.9313466","pdf_url":"https://ieeexplore.ieee.org/ielx7/9312958/9312977/09313466.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8600000143051147,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3121468755.pdf","grobid_xml":"https://content.openalex.org/works/W3121468755.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W648143168","https://openalex.org/W2099471712","https://openalex.org/W2576442780","https://openalex.org/W2914304175","https://openalex.org/W2942354470","https://openalex.org/W2963073614","https://openalex.org/W3002108456","https://openalex.org/W3012846954","https://openalex.org/W3023402713","https://openalex.org/W3024575832","https://openalex.org/W3025576489","https://openalex.org/W3026931681","https://openalex.org/W3037096201","https://openalex.org/W4205829018","https://openalex.org/W6621378261","https://openalex.org/W6806428794"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4391621807","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391621790","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"The":[0,19,96,117,217],"severe":[1],"pneumonia":[2,26,113],"induced":[3,25],"by":[4,222,268],"the":[5,8,15,23,29,33,57,61,85,91,104,111,154,165,179,185,193,202,214,223,227,236,243,247,258,265,269,289,311,317],"infection":[6],"of":[7,22,32,230,252],"SARS-CoV-2":[9,24],"virus":[10],"causes":[11],"massive":[12],"death":[13],"in":[14,56,235,257,276,316],"ongoing":[16],"COVID-19":[17,92,112,130,160,199],"pandemic.":[18],"early":[20],"detection":[21],"relies":[27],"on":[28],"unique":[30],"patterns":[31,109],"chest":[34,87,93,106,114,139,162,167],"XRay":[35],"images.":[36,169],"Deep":[37],"learning":[38,55,238],"is":[39,60,98,151,176,294],"a":[40,72,101,121,207],"data-greedy":[41],"algorithm":[42],"to":[43,63,77,80,90,99,110,157,196,205,233,255,296,309],"achieve":[44],"high":[45],"performance":[46],"when":[47],"adequately":[48],"trained.":[49],"A":[50,147,170],"common":[51,312],"challenge":[52],"for":[53,137,144,153,178,188,213],"machine":[54],"medical":[58,298,318],"domain":[59,89],"accessibility":[62],"properly":[64],"annotated":[65],"data.":[66],"In":[67,263],"this":[68],"study,":[69],"we":[70,191],"apply":[71],"conditional":[73],"adversarial":[74],"network":[75],"(cGAN)":[76],"perform":[78],"image":[79,81,284,299],"(Pix2Pix)":[82],"translation":[83,156],"from":[84,103,164,201,260],"non-COVID-19":[86],"X-Ray":[88,94,107,140,161,204],"domain.":[95,319],"objective":[97],"learn":[100],"mapping":[102],"normal":[105,138,166,203],"visual":[108],"X-ray":[115,168],"patterns.":[116],"original":[118,244],"dataset":[119,210,225,271],"has":[120,134,272],"typical":[122],"imbalanced":[123,245],"issue":[124,315],"because":[125],"it":[126,304],"contains":[127],"only":[128],"219":[129],"positive":[131],"images":[132,136,143,163,200],"but":[133],"1,341":[135],"and":[141,279,303],"1,345":[142],"viral":[145],"pneumonia.":[146],"U-Net":[148],"based":[149,219],"architecture":[150,175],"applied":[152,177],"image-to-image":[155],"generate":[158],"synthesized":[159],"50-convolutional-layer":[171],"residual":[172],"net":[173],"(ResNet)":[174],"final":[180],"classification":[181,215,228],"task.":[182,216],"After":[183],"training":[184,209,259],"GAN":[186,194],"model":[187,248],"100":[189],"epochs,":[190],"use":[192],"generator":[195],"translate":[197],"1,100":[198],"form":[206],"balanced":[208],"(3,762":[211],"images)":[212],"ResNet":[218],"classifier":[220,266],"trained":[221,241,267],"enhanced":[224,270],"achieves":[226,249],"accuracy":[229,251],"97.8%":[231],"compared":[232,254],"96.1%":[234,253],"transfer":[237],"mode.":[239],"When":[240],"with":[242],"dataset,":[246],"an":[250,306],"95.6%":[256],"trainby-scratch":[261],"model.":[262],"addition,":[264],"more":[273],"stable":[274],"measures":[275],"precision,":[277],"recall,":[278],"F1":[280],"scores":[281],"across":[282],"different":[283],"classes.":[285],"We":[286],"conclude":[287],"that":[288],"GAN-based":[290],"data":[291],"enhancement":[292],"strategy":[293],"applicable":[295],"most":[297],"pattern":[300],"recognition":[301],"tasks,":[302],"provides":[305],"effective":[307],"way":[308],"solve":[310],"expertise":[313],"dependence":[314]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}