{"id":"https://openalex.org/W7126053386","doi":"https://doi.org/10.1109/bibm66473.2025.11356774","title":"FedProCL: Prototype-Guided Contrastive Federated Learning for Medical Image Classification Under Concept Shift","display_name":"FedProCL: Prototype-Guided Contrastive Federated Learning for Medical Image Classification Under Concept Shift","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126053386","doi":"https://doi.org/10.1109/bibm66473.2025.11356774"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356774","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5124181616","display_name":"Yipan Wei","orcid":null},"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":"Yipan Wei","raw_affiliation_strings":["School of Computer Science, Wuhan University,Wuhan,China"],"raw_orcid":null,"affiliations":[{"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/A5000591016","display_name":"Yuchen Zou","orcid":"https://orcid.org/0009-0000-4337-7703"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]},{"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":"Yuchen Zou","raw_affiliation_strings":["Academy of Advanced Interdisciplinary Studies, Wuhan University,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy of Advanced Interdisciplinary Studies, Wuhan University,Wuhan,China","institution_ids":["https://openalex.org/I196699116","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124190906","display_name":"Hang Su","orcid":null},"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":"Hang Su","raw_affiliation_strings":["School of Computer Science, Wuhan University,Wuhan,China"],"raw_orcid":null,"affiliations":[{"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/A5081701266","display_name":"Yapeng Li","orcid":"https://orcid.org/0009-0000-2983-885X"},"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":"Yapeng Li","raw_affiliation_strings":["School of Computer Science, Wuhan University,Wuhan,China"],"raw_orcid":null,"affiliations":[{"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/A5124189960","display_name":"Bo Du","orcid":null},"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":["School of Computer Science, Wuhan University,Wuhan,China"],"raw_orcid":null,"affiliations":[{"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":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4236","last_page":"4239"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9271000027656555,"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":0.9271000027656555,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.016300000250339508,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.005900000222027302,"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/federated-learning","display_name":"Federated learning","score":0.7228999733924866},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6330000162124634},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5889000296592712},{"id":"https://openalex.org/keywords/clarity","display_name":"CLARITY","score":0.4754999876022339},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.46880000829696655},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4205999970436096},{"id":"https://openalex.org/keywords/semantic-data-model","display_name":"Semantic data model","score":0.3580000102519989},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.35030001401901245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8169000148773193},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7228999733924866},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6330000162124634},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5889000296592712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5073999762535095},{"id":"https://openalex.org/C2777146004","wikidata":"https://www.wikidata.org/wiki/Q14949826","display_name":"CLARITY","level":2,"score":0.4754999876022339},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3546000123023987},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34060001373291016},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28200000524520874},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2678000032901764},{"id":"https://openalex.org/C37926939","wikidata":"https://www.wikidata.org/wiki/Q7449061","display_name":"Semantic equivalence","level":4,"score":0.2612999975681305},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356774","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1836220556","display_name":null,"funder_award_id":"62225113","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8400922664","display_name":null,"funder_award_id":"2023YFC2705700","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2028138594","https://openalex.org/W2194775991","https://openalex.org/W3012501605","https://openalex.org/W3138516171","https://openalex.org/W3168213397","https://openalex.org/W3182158470","https://openalex.org/W4283796083","https://openalex.org/W4317436377","https://openalex.org/W4402754207"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL),":[2],"as":[3],"a":[4,61],"privacy-preserving":[5],"collaborative":[6],"learning":[7],"paradigm,":[8],"helps":[9],"alleviate":[10],"privacy":[11],"leakage":[12],"and":[13,42,75,89,120],"data":[14],"silos":[15],"challenges.":[16],"However,":[17],"in":[18,92,116,124],"medical":[19],"scenarios,":[20],"it":[21,44],"still":[22],"suffers":[23],"from":[24],"the":[25,47,81,93],"severe":[26],"problem":[27],"of":[28,118,126],"concept":[29],"shift,":[30],"where":[31],"class":[32,53],"distributions":[33],"differ":[34],"significantly":[35,112],"across":[36],"clients,":[37],"leading":[38],"to":[39,50,85],"semantic":[40,98],"inconsistency":[41],"making":[43],"difficult":[45],"for":[46],"global":[48,72],"model":[49,82],"learn":[51],"unified":[52],"representations.":[54],"To":[55],"address":[56],"this,":[57],"we":[58],"propose":[59],"FedProCL,":[60],"prototype-aware":[62],"federated":[63],"contrastive":[64,77],"optimization":[65],"method.":[66],"By":[67],"integrating":[68],"local":[69],"prototype":[70,73],"construction,":[71],"aggregation,":[74],"prototype-level":[76],"constraints":[78],"during":[79],"training,":[80],"is":[83],"guided":[84],"achieve":[86],"intra-class":[87],"compactness":[88],"inter-class":[90],"separability":[91],"feature":[94,127],"space,":[95],"thereby":[96],"enhancing":[97],"alignment.":[99],"We":[100],"conduct":[101],"systematic":[102],"evaluations":[103],"on":[104],"multiple":[105],"datasets.":[106],"Experimental":[107],"results":[108],"show":[109],"that":[110],"FedProCL":[111],"outperforms":[113],"existing":[114],"methods":[115],"terms":[117],"accuracy":[119],"also":[121],"demonstrates":[122],"advantages":[123],"clarity":[125],"representation.":[128]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}