{"id":"https://openalex.org/W7160079713","doi":"https://doi.org/10.48550/arxiv.2605.00647","title":"Label-Conditioned Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Curriculum-Gated Contrastive Alignment","display_name":"Label-Conditioned Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Curriculum-Gated Contrastive Alignment","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160079713","doi":"https://doi.org/10.48550/arxiv.2605.00647"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00647","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135150732","display_name":"Xinran Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xinran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619888","display_name":"Yuwen Li","orcid":"https://orcid.org/0000-0003-3060-836X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135260313","display_name":"Hongxiang Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Hongxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135246234","display_name":"Heyang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Heyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135116972","display_name":"Jianqing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jianqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135173277","display_name":"Zongmin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zongmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135219342","display_name":"Chengyu Liu","orcid":"https://orcid.org/0000-0003-1965-3020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9401999711990356,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9401999711990356,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.008299999870359898,"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/T13248","display_name":"Healthcare Technology and Patient Monitoring","score":0.0027000000700354576,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5146999955177307},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5077000260353088},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4359999895095825},{"id":"https://openalex.org/keywords/clinical-practice","display_name":"Clinical Practice","score":0.35989999771118164},{"id":"https://openalex.org/keywords/diagnostic-accuracy","display_name":"Diagnostic accuracy","score":0.32010000944137573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.31520000100135803}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5666999816894531},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5146999955177307},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5078999996185303},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4375},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37689998745918274},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32280001044273376},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C2983449737","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Clinical diagnosis","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"pediatric":[1,28,54,80,196],"electrocardiogram":[2],"(ECG)":[3],"interpretation":[4],"remains":[5],"challenging":[6],"because":[7],"developmental":[8],"differences":[9],"in":[10],"heart":[11],"rate,":[12],"intervals,":[13],"and":[14,51,70,107,112,120,127,156,162,189],"waveforms":[15],"limit":[16],"the":[17],"transferability":[18],"of":[19,104],"models":[20],"trained":[21],"mainly":[22],"on":[23,48],"adult":[24],"data,":[25],"while":[26,201],"expert-labeled":[27],"ECG":[29,37,44,65,95,171,183],"cohorts":[30],"are":[31],"scarce.":[32],"We":[33],"propose":[34],"PEACE":[35,56,100],"(Pediatric-Adult":[36],"Alignment":[38],"via":[39],"Cross-modal":[40],"Enhancement),":[41],"an":[42],"adult-to-pediatric":[43],"transfer":[45],"framework":[46],"pretrained":[47],"MIMIC-IV":[49],"ECGs":[50],"adapted":[52],"to":[53,63,75],"targets.":[55],"integrates":[57],"label-specific":[58],"bidirectional":[59],"contrastive":[60],"learning":[61],"(LSBC)":[62],"align":[64],"representations":[66],"with":[67,143,170,186],"diagnostic":[68],"semantics":[69],"curriculum":[71],"adaptive":[72],"fusion":[73],"(CAF)":[74],"stabilize":[76],"optimization":[77],"under":[78,109,198],"limited":[79],"supervision.":[81],"Label-conditioned":[82],"short":[83],"text":[84],"descriptors":[85,193],"provide":[86],"auxiliary":[87],"semantic":[88,192],"supervision":[89],"during":[90,175],"training,":[91],"whereas":[92],"inference":[93],"requires":[94],"signals":[96],"only.":[97],"On":[98,129],"ZZU-pECG,":[99],"achieves":[101],"macro-average":[102,134],"AUCs":[103],"59.39%,":[105],"81.74%,":[106],"91.56%":[108],"zero-shot,":[110],"50-shot,":[111],"full":[113,137],"fine-tuning":[114,138],"settings,":[115],"respectively,":[116],"outperforming":[117],"ECG-only,":[118],"multimodal,":[119],"generic":[121],"domain":[122],"adaptation":[123],"baselines":[124],"including":[125],"DANN":[126],"MMD.":[128],"PTB-XL,":[130],"it":[131],"reaches":[132],"96.90%":[133],"AUC":[135],"after":[136],"over":[139],"nine":[140],"harmonized":[141],"labels":[142],"nonzero":[144],"mapped":[145],"incidence.":[146],"Gradient-based":[147],"attention":[148],"maps":[149],"show":[150],"increased":[151],"saliency":[152],"around":[153,163],"QRS":[154],"voltage":[155],"morphology":[157],"regions":[158,172],"for":[159,166],"chamber-related":[160],"RVH":[161],"QRS-to-T/repolarization":[164],"intervals":[165],"LQTS,":[167],"broadly":[168],"consistent":[169],"commonly":[173],"inspected":[174],"routine":[176],"interpretation.":[177],"These":[178],"results":[179],"suggest":[180],"that":[181],"adult-scale":[182],"pretraining":[184],"coupled":[185],"rhythm,":[187],"morphology,":[188],"ST-T":[190],"repolarization":[191],"improves":[194],"transferable":[195],"diagnosis":[197],"label":[199],"scarcity":[200],"preserving":[202],"clinically":[203],"interpretable":[204],"waveform":[205],"focus.":[206]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-05T00:00:00"}