{"id":"https://openalex.org/W7148891890","doi":"https://doi.org/10.48550/arxiv.2604.01498","title":"Semantic Compensation via Adversarial Removal for Robust Zero-Shot ECG Diagnosis","display_name":"Semantic Compensation via Adversarial Removal for Robust Zero-Shot ECG Diagnosis","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7148891890","doi":"https://doi.org/10.48550/arxiv.2604.01498"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01498","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01498","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.01498","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132906679","display_name":"Hongjun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132860330","display_name":"Rujun Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Rujun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132841249","display_name":"Leyu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Leyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132858423","display_name":"Chao Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Chao","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":0.8019000291824341,"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":0.8019000291824341,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.05090000107884407,"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/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.018799999728798866,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7807000279426575},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5475999712944031},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5216000080108643},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4975999891757965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4869000017642975}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7807000279426575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7445999979972839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6887000203132629},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5475999712944031},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4975999891757965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.41110000014305115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38600000739097595},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3580000102519989},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01498","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01498","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.01498","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01498","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7850481271743774,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"ECG--language":[1,68],"pretraining":[2,69],"methods":[3],"enable":[4],"zero-shot":[5],"diagnosis":[6],"by":[7,80],"aligning":[8],"cardiac":[9],"signals":[10],"with":[11,89,131,150,166,214],"clinical":[12,132],"text,":[13],"but":[14],"they":[15],"do":[16],"not":[17],"explicitly":[18,81],"model":[19,84],"robustness":[20,79,175,207],"to":[21,45,85,94,110,124,158],"partial":[22],"observation":[23],"and":[24,93,164,211],"are":[25],"typically":[26],"studied":[27],"under":[28,195,208],"fully":[29],"observed":[30],"ECG":[31,116,122,148],"settings.":[32],"In":[33,60],"practice,":[34],"diagnostically":[35,169],"critical":[36,91],"leads":[37],"or":[38,50],"temporal":[39,212],"segments":[40],"may":[41],"be":[42],"missing":[43],"due":[44],"electrode":[46],"detachment,":[47],"motion":[48],"artifacts,":[49],"signal":[51],"corruption,":[52,144],"causing":[53],"severe":[54],"degradation":[55],"of":[56],"cross-modal":[57],"semantic":[58,206],"alignment.":[59],"this":[61],"paper,":[62],"we":[63,104,145,179],"propose":[64],"\\textbf{SCAR},":[65],"a":[66,106,151],"robust":[67],"framework":[70],"for":[71],"\\textbf{S}emantic":[72],"\\textbf{C}ompensation":[73],"via":[74],"\\textbf{A}dversarial":[75],"\\textbf{R}emoval.":[76],"SCAR":[77,203],"improves":[78,205],"training":[82],"the":[83,99,112,121,147,160],"remain":[86,128],"semantically":[87,90,129,152],"aligned":[88,130],"missingness":[92],"recover":[95],"diagnostic":[96,137,193,223],"meaning":[97],"from":[98],"remaining":[100,161],"visible":[101,162],"evidence.":[102],"Specifically,":[103],"introduce":[105,181],"differentiable":[107],"adversarial":[108,143],"masker":[109],"remove":[111],"most":[113],"alignment-critical":[114],"spatio-temporal":[115],"tokens":[117,163],"during":[118],"training,":[119],"forcing":[120],"encoder":[123,149],"learn":[125],"representations":[126],"that":[127,156,202],"text":[133],"even":[134],"when":[135],"primary":[136,222],"evidence":[138,224],"is":[139,225],"missing.":[140],"Under":[141],"such":[142],"equip":[146],"supervised":[153],"adaptive":[154],"selector":[155],"learns":[157],"reweight":[159],"compensate":[165],"secondary":[167],"yet":[168],"informative":[170],"morphological":[171],"cues.":[172],"To":[173],"evaluate":[174],"beyond":[176],"classification":[177],"accuracy,":[178],"further":[180],"Counterfactual":[182],"Missingness":[183],"Resolution":[184],"Score":[185],"(CMRS),":[186],"which":[187],"quantifies":[188],"how":[189],"well":[190],"feature":[191],"preserve":[192],"semantics":[194],"missingness.":[196],"Experiments":[197],"on":[198],"$6$":[199],"datasets":[200],"show":[201],"consistently":[204],"joint":[209],"lead":[210],"missingness,":[213],"particularly":[215],"clear":[216],"advantages":[217],"in":[218],"harder":[219],"cases":[220],"where":[221],"unavailable,":[226],"while":[227],"also":[228],"yielding":[229],"stronger":[230],"linear-probing":[231],"transferability.":[232]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-04T00:00:00"}