{"id":"https://openalex.org/W4391165366","doi":"https://doi.org/10.1109/jbhi.2024.3357947","title":"Dense Contrastive-Based Federated Learning for Dense Prediction Tasks on Medical Images","display_name":"Dense Contrastive-Based Federated Learning for Dense Prediction Tasks on Medical Images","publication_year":2024,"publication_date":"2024-01-24","ids":{"openalex":"https://openalex.org/W4391165366","doi":"https://doi.org/10.1109/jbhi.2024.3357947","pmid":"https://pubmed.ncbi.nlm.nih.gov/38265899"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2024.3357947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2024.3357947","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5040019549","display_name":"Yuning Yang","orcid":"https://orcid.org/0009-0001-2570-0017"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuning Yang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-2570-0017","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100321192","display_name":"Xiaohong Liu","orcid":"https://orcid.org/0000-0002-0818-1059"},"institutions":[{"id":"https://openalex.org/I4210147892","display_name":"CRUK Lung Cancer Centre of Excellence","ror":"https://ror.org/04nm2mq63","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210147892"]},{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaohong Liu","raw_affiliation_strings":["UCL Cancer Institute, University College London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0002-0818-1059","affiliations":[{"raw_affiliation_string":"UCL Cancer Institute, University College London, London, U.K","institution_ids":["https://openalex.org/I4210147892","https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103284688","display_name":"Tianrun Gao","orcid":"https://orcid.org/0009-0002-6063-1034"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrun Gao","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-6063-1034","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061030501","display_name":"Xiaodong Xu","orcid":"https://orcid.org/0000-0003-4245-5989"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Xu","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4245-5989","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405787","display_name":"Ping Zhang","orcid":"https://orcid.org/0000-0002-0269-104X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Zhang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0269-104X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100458577","display_name":"Guangyu Wang","orcid":"https://orcid.org/0000-0003-1312-884X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyu Wang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1312-884X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0012,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.95658545,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"28","issue":"4","first_page":"2055","last_page":"2066"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9984999895095825,"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/computer-science","display_name":"Computer science","score":0.8410488367080688},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6546213626861572},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5958350300788879},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5638290047645569},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5425494313240051},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5257666707038879},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.45156151056289673},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44027653336524963},{"id":"https://openalex.org/keywords/learning-object","display_name":"Learning object","score":0.43556880950927734},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4281498193740845},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.42130929231643677},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4105266332626343}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8410488367080688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6546213626861572},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5958350300788879},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5638290047645569},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5425494313240051},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5257666707038879},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.45156151056289673},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44027653336524963},{"id":"https://openalex.org/C2779542340","wikidata":"https://www.wikidata.org/wiki/Q1062461","display_name":"Learning object","level":2,"score":0.43556880950927734},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4281498193740845},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.42130929231643677},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4105266332626343},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2024.3357947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2024.3357947","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:38265899","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38265899","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G7835006035","display_name":null,"funder_award_id":"62272055","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2163605009","https://openalex.org/W2541884796","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2747257024","https://openalex.org/W2769848455","https://openalex.org/W2798991696","https://openalex.org/W2807006176","https://openalex.org/W2956228567","https://openalex.org/W2963037989","https://openalex.org/W2963889845","https://openalex.org/W2981337066","https://openalex.org/W2989289980","https://openalex.org/W2990789643","https://openalex.org/W3015209252","https://openalex.org/W3018105153","https://openalex.org/W3029897895","https://openalex.org/W3036550469","https://openalex.org/W3038022836","https://openalex.org/W3083403771","https://openalex.org/W3098488418","https://openalex.org/W3108262825","https://openalex.org/W3124515033","https://openalex.org/W3127299377","https://openalex.org/W3128661784","https://openalex.org/W3129603732","https://openalex.org/W3132455321","https://openalex.org/W3155912831","https://openalex.org/W3159080474","https://openalex.org/W3167387588","https://openalex.org/W3168822201","https://openalex.org/W3180884825","https://openalex.org/W3182158470","https://openalex.org/W3185102306","https://openalex.org/W3190214286","https://openalex.org/W3213582899","https://openalex.org/W3217532475","https://openalex.org/W4206320562","https://openalex.org/W4214520160","https://openalex.org/W4224227775","https://openalex.org/W4225978071","https://openalex.org/W4287332481","https://openalex.org/W4293237729","https://openalex.org/W4293584584","https://openalex.org/W4295312788","https://openalex.org/W4305008618","https://openalex.org/W4310456609","https://openalex.org/W4312751731","https://openalex.org/W4312950667","https://openalex.org/W4313042636","https://openalex.org/W4318619660","https://openalex.org/W4390872707","https://openalex.org/W6620707391","https://openalex.org/W6684191040","https://openalex.org/W6750227808","https://openalex.org/W6752029299","https://openalex.org/W6759238902","https://openalex.org/W6766978945","https://openalex.org/W6769363519","https://openalex.org/W6770590064","https://openalex.org/W6780008666","https://openalex.org/W6781419859","https://openalex.org/W6785786191","https://openalex.org/W6789100154","https://openalex.org/W6789305514","https://openalex.org/W6796792687","https://openalex.org/W6838907135","https://openalex.org/W6846126510"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3048601286","https://openalex.org/W2965925734","https://openalex.org/W3000197790"],"abstract_inverted_index":{"Deep":[0],"learning":[1,37,115,129,205],"(DL)":[2],"models":[3,48],"have":[4,95],"achieved":[5],"remarkable":[6],"success":[7],"in":[8,28,55,122,178,207,221],"various":[9],"domains.":[10],"But":[11],"training":[12],"an":[13,91,196],"accurate":[14],"DL":[15],"model":[16],"requires":[17],"large":[18],"amounts":[19],"of":[20,50,60,90,146,159,187],"data,":[21],"which":[22,132],"can":[23],"be":[24],"challenging":[25],"to":[26,32,64,75,130,155],"obtain":[27],"medical":[29,56],"settings":[30,57],"due":[31,63],"privacy":[33],"concerns.":[34],"Recently,":[35],"federated":[36,114,204],"(FL)":[38],"has":[39],"emerged":[40],"as":[41,104],"a":[42,185],"promising":[43],"solution":[44],"that":[45,86],"shares":[46],"local":[47,135,152],"instead":[49],"raw":[51],"data.":[52],"However,":[53],"FL":[54,123],"faces":[58],"challenges":[59],"client":[61],"drift":[62],"the":[65,83,99,134,139,144,149,157,202,210],"data":[66],"heterogeneity":[67],"across":[68],"dispersed":[69],"institutions.":[70],"Although":[71],"there":[72],"exist":[73],"studies":[74],"address":[76],"this":[77,108],"challenge,":[78],"they":[79],"mainly":[80],"focus":[81],"on":[82,98,184],"classification":[84],"tasks":[85,121],"learn":[87],"global":[88,140,150],"representation":[89,170],"entire":[92],"image.":[93],"Few":[94],"been":[96],"studied":[97],"dense":[100,112,119,127,160,176],"prediction":[101,120,162],"tasks,":[102],"such":[103],"object":[105],"detection.":[106,193],"In":[107],"study,":[109],"we":[110],"propose":[111],"contrastive-based":[113],"(DCFL)":[116],"tailored":[117],"for":[118,190],"settings.":[124,224],"DCFL":[125,166,183,194],"introduces":[126],"contrastive":[128,169,179],"FL,":[131],"aligns":[133],"optimization":[136],"objectives":[137],"towards":[138],"objective":[141],"by":[142,171,214,219],"maximizing":[143],"agreement":[145],"representations":[147,174],"between":[148],"and":[151,216],"models.":[153],"Moreover,":[154],"improve":[156],"performance":[158,198],"target":[161],"at":[163],"each":[164],"level,":[165],"applies":[167],"multi-scale":[168,173],"utilizing":[172],"with":[175,201],"features":[177],"learning.":[180],"We":[181],"evaluated":[182],"set":[186],"realistic":[188],"datasets":[189],"pulmonary":[191],"nodule":[192],"demonstrates":[195],"overall":[197],"improvement":[199],"compared":[200],"other":[203],"methods":[206],"heterogeneous":[208,223],"settings-improving":[209],"mean":[211],"average":[212],"precision":[213],"4.13%":[215],"testing":[217],"recall":[218],"6.07%":[220],"highly":[222]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}