{"id":"https://openalex.org/W2963243407","doi":"https://doi.org/10.1109/isbi.2019.8759442","title":"Abnormal Chest X-Ray Identification With Generative Adversarial One-Class Classifier","display_name":"Abnormal Chest X-Ray Identification With Generative Adversarial One-Class Classifier","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2963243407","doi":"https://doi.org/10.1109/isbi.2019.8759442","mag":"2963243407"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037675893","display_name":"Yuxing Tang","orcid":"https://orcid.org/0000-0002-6452-2751"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu-Xing Tang","raw_affiliation_strings":["Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health (NIH) Clinical Center, Bethesda, USA"],"affiliations":[{"raw_affiliation_string":"Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health (NIH) Clinical Center, Bethesda, USA","institution_ids":["https://openalex.org/I4210155647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081658414","display_name":"Youbao Tang","orcid":"https://orcid.org/0000-0001-8719-3375"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"You-Bao Tang","raw_affiliation_strings":["Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health (NIH) Clinical Center, Bethesda, USA"],"affiliations":[{"raw_affiliation_string":"Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health (NIH) Clinical Center, Bethesda, USA","institution_ids":["https://openalex.org/I4210155647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100774500","display_name":"Mei Han","orcid":"https://orcid.org/0000-0002-3123-2205"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mei Han","raw_affiliation_strings":["Ping An Technology, US Research Labs, USA"],"affiliations":[{"raw_affiliation_string":"Ping An Technology, US Research Labs, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016038454","display_name":"Jing Xiao","orcid":"https://orcid.org/0000-0001-9615-4749"},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xiao","raw_affiliation_strings":["Ping An Technology Co., Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Ping An Technology Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4401726822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016047550","display_name":"Ronald M. Summers","orcid":"https://orcid.org/0000-0001-8081-7376"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ronald M. Summers","raw_affiliation_strings":["Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health (NIH) Clinical Center, Bethesda, USA"],"affiliations":[{"raw_affiliation_string":"Imaging Biomarkers and Computer-Aided Diagnosis Lab, Radiology and Imaging Sciences, National Institutes of Health (NIH) Clinical Center, Bethesda, USA","institution_ids":["https://openalex.org/I4210155647"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5037675893"],"corresponding_institution_ids":["https://openalex.org/I4210155647"],"apc_list":null,"apc_paid":null,"fwci":7.8064,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.97888637,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1358","last_page":"1361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9993000030517578,"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":0.9993000030517578,"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/T11894","display_name":"Radiology practices and education","score":0.9747999906539917,"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/T13018","display_name":"Seismology and Earthquake Studies","score":0.9455999732017517,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6989952325820923},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6875849366188049},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.6381006836891174},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6380084156990051},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.5393253564834595},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5106073021888733},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4815378785133362},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41874194145202637},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4173867404460907},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.41720888018608093},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.41584303975105286},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.41006743907928467},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3734055757522583},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3415900468826294},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.33758389949798584},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15823987126350403}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6989952325820923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6875849366188049},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.6381006836891174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6380084156990051},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.5393253564834595},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5106073021888733},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4815378785133362},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41874194145202637},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4173867404460907},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.41720888018608093},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.41584303975105286},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.41006743907928467},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3734055757522583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3415900468826294},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.33758389949798584},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15823987126350403},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2019.8759442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320337376","display_name":"NIH Clinical Center","ror":"https://ror.org/04vfsmv21"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2074399002","https://openalex.org/W2099471712","https://openalex.org/W2611650229","https://openalex.org/W2806321514","https://openalex.org/W2806794719","https://openalex.org/W2886327376","https://openalex.org/W2962816100","https://openalex.org/W2963061824","https://openalex.org/W3101156210","https://openalex.org/W4320013936","https://openalex.org/W6639824700"],"related_works":["https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W3178813832","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2971552217","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4226298148"],"abstract_inverted_index":{"Being":[0],"one":[1],"of":[2,14,55,61,76,144,150],"the":[3,18,72,77,87,94,102,109,114,117,140,155,167,171],"most":[4],"common":[5],"diagnostic":[6],"imaging":[7],"tests,":[8],"chest":[9,30,46,79,83,130],"radiography":[10],"requires":[11],"timely":[12],"reporting":[13],"potential":[15,168],"findings":[16],"in":[17,86,113,122,161,169],"images.":[19],"In":[20],"this":[21],"paper,":[22],"we":[23],"propose":[24],"an":[25,148],"end-to-end":[26],"architecture":[27,52,96],"for":[28,173],"abnormal":[29,129],"X-ray":[31,47,84,159],"identification":[32],"using":[33],"generative":[34],"adversarial":[35],"one-class":[36,163],"learning.":[37],"Unlike":[38],"previous":[39],"approaches,":[40],"our":[41,145],"method":[42],"takes":[43],"only":[44],"normal":[45,78,133],"images":[48],"as":[49],"input.":[50],"The":[51],"is":[53,92,106,111,152],"composed":[54],"three":[56],"deep":[57],"neural":[58],"networks,":[59],"each":[60],"which":[62],"learned":[63,95],"by":[64],"competing":[65],"while":[66],"collaborating":[67],"among":[68],"them":[69],"to":[70],"model":[71,99,118],"underlying":[73],"content":[74,110],"structure":[75],"X-rays.":[80],"Given":[81],"a":[82,162],"image":[85],"testing":[88],"phase,":[89,116],"if":[90,104],"it":[91,105],"normal,":[93],"can":[97],"well":[98],"and":[100,136,142],"reconstruct":[101],"content;":[103],"abnormal,":[107],"since":[108],"unseen":[112],"training":[115],"would":[119],"perform":[120],"poorly":[121],"its":[123],"reconstruction.":[124],"It":[125],"thus":[126],"enables":[127],"distinguishing":[128],"X-rays":[131],"from":[132],"ones.":[134],"Quantitative":[135],"qualitative":[137],"experiments":[138],"demonstrate":[139],"effectiveness":[141],"efficiency":[143],"approach,":[146],"where":[147],"AUC":[149],"0.841":[151],"achieved":[153],"on":[154],"challenging":[156],"NIH":[157],"Chest":[158],"dataset":[160],"learning":[164],"setting,":[165],"with":[166],"reducing":[170],"workload":[172],"radiologists.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":2}],"updated_date":"2026-02-28T06:14:18.631764","created_date":"2025-10-10T00:00:00"}