{"id":"https://openalex.org/W4390971364","doi":"https://doi.org/10.1109/bibm58861.2023.10385361","title":"FedARC: Federated Learning for Multi-Center Tuberculosis Chest X-ray Diagnosis with Adaptive Regularizing Contrastive Representation","display_name":"FedARC: Federated Learning for Multi-Center Tuberculosis Chest X-ray Diagnosis with Adaptive Regularizing Contrastive Representation","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390971364","doi":"https://doi.org/10.1109/bibm58861.2023.10385361"},"language":"en","primary_location":{"id":"doi:10.1109/bibm58861.2023.10385361","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101434532","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0002-1542-0257"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Liu","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China","School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039158745","display_name":"Yong Luo","orcid":"https://orcid.org/0000-0002-2296-6370"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Luo","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China","School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082564408","display_name":"Yongchao Xu","orcid":"https://orcid.org/0000-0002-7253-3151"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongchao Xu","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China","School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060042752","display_name":"Bo Du","orcid":"https://orcid.org/0000-0002-0059-8458"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Du","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China","School of Computer Science, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2125","last_page":"2128"},"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/T11381","display_name":"Infectious Diseases and Tuberculosis","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10038","display_name":"Tuberculosis Research and Epidemiology","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2725","display_name":"Infectious Diseases"},"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":0.7358507513999939},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6385449767112732},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5580416917800903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5351349711418152},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.48141738772392273},{"id":"https://openalex.org/keywords/data-center","display_name":"Data center","score":0.4543147087097168},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.4540267586708069},{"id":"https://openalex.org/keywords/tuberculosis","display_name":"Tuberculosis","score":0.44305863976478577},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4399834871292114},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3233352601528168},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15288177132606506},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.09147879481315613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7358507513999939},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6385449767112732},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5580416917800903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5351349711418152},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48141738772392273},{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.4543147087097168},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.4540267586708069},{"id":"https://openalex.org/C2781069245","wikidata":"https://www.wikidata.org/wiki/Q12204","display_name":"Tuberculosis","level":2,"score":0.44305863976478577},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4399834871292114},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3233352601528168},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15288177132606506},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.09147879481315613},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/bibm58861.2023.10385361","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1904878066","https://openalex.org/W1976863970","https://openalex.org/W2010382953","https://openalex.org/W2194775991","https://openalex.org/W2955213239","https://openalex.org/W2963163009","https://openalex.org/W2963819344","https://openalex.org/W2990789643","https://openalex.org/W3034922525","https://openalex.org/W3129603732","https://openalex.org/W3168269241","https://openalex.org/W3182158470","https://openalex.org/W4224227775","https://openalex.org/W4283810430","https://openalex.org/W4285071899","https://openalex.org/W4287906413","https://openalex.org/W4300427714","https://openalex.org/W4306248755","https://openalex.org/W4386076493","https://openalex.org/W6639867372","https://openalex.org/W6728757088","https://openalex.org/W6738383168","https://openalex.org/W6765541894","https://openalex.org/W6770590064","https://openalex.org/W6772318479","https://openalex.org/W6773817997","https://openalex.org/W6789305514","https://openalex.org/W6791102956","https://openalex.org/W6796484261","https://openalex.org/W6796504275","https://openalex.org/W6839362776","https://openalex.org/W6854708048"],"related_works":["https://openalex.org/W2806061655","https://openalex.org/W1955737261","https://openalex.org/W2775573077","https://openalex.org/W4249377076","https://openalex.org/W2911461202","https://openalex.org/W4210389441","https://openalex.org/W2765354416","https://openalex.org/W1965825568","https://openalex.org/W2110949356","https://openalex.org/W1910186363"],"abstract_inverted_index":{"Tuberculosis":[0],"(TB)":[1],"poses":[2],"a":[3,29,44,54,59,110],"significant":[4,193],"global":[5,146,156],"health":[6],"threat":[7],"and":[8,18],"leads":[9],"to":[10,27,96,116,137,152,175],"millions":[11],"of":[12,53,62,144,164,168,195],"deaths":[13],"annually.":[14],"While":[15],"early":[16],"diagnosis":[17],"treatment":[19],"can":[20],"substantially":[21],"enhance":[22],"survival":[23],"prospects,":[24],"it":[25,139],"continues":[26],"present":[28],"major":[30],"challenge,":[31],"particularly":[32],"in":[33,100,202],"developing":[34],"countries.":[35],"In":[36],"recent":[37],"years,":[38],"machine":[39],"learning":[40,81,160],"has":[41],"emerged":[42],"as":[43],"valuable":[45],"tool":[46],"for":[47,131],"tuberculosis":[48],"diagnosis.":[49,85],"However,":[50,86],"the":[51,97,128,141,145,150,155,162,165,172,192],"training":[52],"dependable":[55],"diagnostic":[56],"model":[57,151,174],"necessitates":[58],"large":[60],"volume":[61],"data,":[63],"typically":[64],"distributed":[65],"across":[66,74,104,124],"multiple":[67],"medical":[68],"centers.":[69,106],"To":[70,120],"safeguard":[71],"data":[72,102,122],"privacy":[73],"various":[75],"centers,":[76,125],"we":[77,108,126],"have":[78],"incorporated":[79],"federated":[80],"(FL)":[82],"into":[83],"TB":[84,101],"conventional":[87],"FL":[88,113],"methods":[89,201],"suffer":[90],"from":[91],"substantial":[92],"performance":[93],"degradation":[94],"due":[95],"considerable":[98],"variation":[99],"distribution":[103],"different":[105],"Consequently,":[107],"introduce":[109],"novel":[111],"personalized":[112],"approach,":[114],"FedARC,":[115],"address":[117],"this":[118],"issue.":[119],"mitigate":[121],"heterogeneity":[123],"guide":[127],"objective":[129],"function":[130],"each":[132,169],"center":[133],"with":[134,140],"adaptive":[135],"regularization":[136],"align":[138],"stationary":[142],"point":[143],"loss,":[147],"thereby":[148],"enabling":[149,171],"converge":[153],"towards":[154],"optimum.":[157],"Simultaneously,":[158],"model-contrastive":[159],"enables":[161],"exploration":[163],"specific":[166],"attributes":[167],"client,":[170],"local":[173],"learn":[176],"more":[177],"generalizable":[178],"features.":[179],"Extensive":[180],"experimental":[181],"results":[182],"on":[183],"five":[184],"publicly":[185],"available":[186],"chest":[187],"X-ray":[188],"image":[189],"datasets":[190],"demonstrate":[191],"outperformance":[194],"our":[196],"proposed":[197],"method":[198],"over":[199],"state-of-the-art":[200],"diverse":[203],"settings.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
