{"id":"https://openalex.org/W7126108080","doi":"https://doi.org/10.1109/bibm66473.2025.11356974","title":"Personalizing Federated Learning Guided by Site-Aggregated Representation for Multi-Site One-Shot Medical Image Segmentation","display_name":"Personalizing Federated Learning Guided by Site-Aggregated Representation for Multi-Site One-Shot Medical Image Segmentation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126108080","doi":"https://doi.org/10.1109/bibm66473.2025.11356974"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356974","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":"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/A5108047889","display_name":"Y\u00ec W\u00e1ng","orcid":"https://orcid.org/0000-0001-5697-0717"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jia Wang","raw_affiliation_strings":["School of Software Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Software Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039567650","display_name":"Yuchen Sun","orcid":"https://orcid.org/0000-0001-7482-1651"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Sun","raw_affiliation_strings":["School of Software Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Software Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124280206","display_name":"Yunan Mei","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunan Mei","raw_affiliation_strings":["School of Software Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Software Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100603887","display_name":"Zihao Xu","orcid":"https://orcid.org/0009-0004-8276-5417"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Xu","raw_affiliation_strings":["School of Software Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Software Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124198585","display_name":"Xin Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Fan","raw_affiliation_strings":["School of Software Technology, Dalian University of Technology,Dalian,China"],"affiliations":[{"raw_affiliation_string":"School of Software Technology, Dalian University of Technology,Dalian,China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108047889"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87421744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7113","last_page":"7120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.40630000829696655,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.40630000829696655,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.32120001316070557,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11448","display_name":"Face recognition and analysis","score":0.051899999380111694,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/segmentation","display_name":"Segmentation","score":0.6700999736785889},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.642300009727478},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5825999975204468},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5644000172615051},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5544999837875366},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.542900025844574},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.44839999079704285},{"id":"https://openalex.org/keywords/synchronizing","display_name":"Synchronizing","score":0.4392000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8222000002861023},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6700999736785889},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.642300009727478},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5825999975204468},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5533999800682068},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.542900025844574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45170000195503235},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C162932704","wikidata":"https://www.wikidata.org/wiki/Q1058791","display_name":"Synchronizing","level":3,"score":0.4392000138759613},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3776000142097473},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34470000863075256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33709999918937683},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.30720001459121704},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26179999113082886}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356974","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356974","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2070949043","https://openalex.org/W2150534249","https://openalex.org/W2167868121","https://openalex.org/W2805285736","https://openalex.org/W2891631795","https://openalex.org/W2979378912","https://openalex.org/W2979457045","https://openalex.org/W2995022099","https://openalex.org/W3096117505","https://openalex.org/W3182158470","https://openalex.org/W3197078253","https://openalex.org/W4288064428","https://openalex.org/W4321760728","https://openalex.org/W4366996414","https://openalex.org/W4386075719","https://openalex.org/W4386113273","https://openalex.org/W4387211643","https://openalex.org/W4402716289","https://openalex.org/W4402727051","https://openalex.org/W4402727305"],"related_works":[],"abstract_inverted_index":{"Personalized":[0],"federated":[1,114],"learning":[2,67],"for":[3,30,89],"medical":[4,55,92],"image":[5,93],"segmentation":[6,103],"enables":[7],"collaborative":[8],"model":[9,25,120],"training":[10],"across":[11],"multiple":[12],"clinical":[13],"sites":[14],"without":[15],"sharing":[16],"patient":[17],"data,":[18],"by":[19,85,113,151],"synchronizing":[20],"a":[21,80],"subset":[22],"of":[23],"global":[24],"parameters":[26],"while":[27],"retaining":[28],"others":[29],"local":[31,65,72],"adaptation.":[32],"However,":[33],"previous":[34],"methods":[35,62,185],"adopt":[36,157],"paired":[37],"labeled":[38],"images":[39],"to":[40,47,53,101,108,144,162],"train":[41],"the":[42,97,121,130,137,164,178],"model,":[43],"which":[44,95,116],"is":[45],"hard":[46],"apply":[48],"in":[49,132],"real":[50],"scenarios":[51],"due":[52],"time-consuming":[54],"experts":[56],"on":[57,64,172,186],"manual":[58],"annotation.":[59],"Additionally,":[60],"these":[61,76],"focus":[63],"parameter":[66],"ignoring":[68],"inter-site":[69],"consistencies":[70],"during":[71],"training.":[73],"To":[74],"address":[75],"challenges,":[77],"we":[78,106,141],"propose":[79,107,143],"personalizing":[81],"Federated":[82],"framework":[83],"guided":[84],"Site-aggregated":[86],"Representation":[87],"(FedSR)":[88],"multi-site":[90,125,149],"one-shot":[91,183],"segmentation,":[94],"exploits":[96],"site-invariant":[98],"latent":[99,160],"information":[100,153],"boost":[102],"performance.":[104],"Specifically,":[105],"learn":[109,145],"an":[110,133],"omniscient":[111],"encoder":[112],"learning,":[115],"can":[117],"not":[118],"only":[119],"data":[122,150],"distribution":[123],"between":[124,148],"datasets":[126],"but":[127],"also":[128],"adapt":[129],"multi-task":[131],"efficient":[134],"way.":[135],"With":[136],"learned":[138],"robust":[139],"representation,":[140],"further":[142],"site-aggregated":[146,159],"representation":[147,161],"mutual":[152],"maximization,":[154],"and":[155],"then":[156],"such":[158],"guide":[163],"personalized":[165],"dual-task":[166],"head":[167],"decoder.":[168],"Extensive":[169],"experiments":[170],"conducted":[171],"two":[173],"MIS":[174,184],"tasks":[175],"demonstrate":[176],"that":[177],"proposed":[179],"FedSR":[180],"outperforms":[181],"state-of-the-art":[182],"segmentation.":[187]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}