{"id":"https://openalex.org/W7125974915","doi":"https://doi.org/10.1109/smc58881.2025.11343186","title":"ACFormer: A Multimodal Attention and Contrastive Learning Framework for Chest Disease Risk Prediction","display_name":"ACFormer: A Multimodal Attention and Contrastive Learning Framework for Chest Disease Risk Prediction","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125974915","doi":"https://doi.org/10.1109/smc58881.2025.11343186"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343186","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 Systems, Man, and Cybernetics (SMC)","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/A5124126500","display_name":"Tao Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tao Lin","raw_affiliation_strings":["Shanghai Institute of Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Technology,Shanghai,China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033162110","display_name":"Yiheng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiheng Zhang","raw_affiliation_strings":["Shanghai Institute of Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Technology,Shanghai,China","institution_ids":["https://openalex.org/I67001856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124126500"],"corresponding_institution_ids":["https://openalex.org/I67001856"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73069273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1799","last_page":"1804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.3864000141620636,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.3864000141620636,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.2540999948978424,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.08829999715089798,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/consistency","display_name":"Consistency (knowledge bases)","score":0.5310999751091003},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5095000267028809},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4652999937534332},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4302000105381012},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.41839998960494995},{"id":"https://openalex.org/keywords/clinical-practice","display_name":"Clinical Practice","score":0.3765999972820282},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.37619999051094055},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.3671000003814697}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6218000054359436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5961999893188477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5339999794960022},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5310999751091003},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5095000267028809},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3522000014781952},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.25200000405311584},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343186","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2963409068","https://openalex.org/W2990263762","https://openalex.org/W2995225687","https://openalex.org/W3004707432","https://openalex.org/W3034124771","https://openalex.org/W3082554910","https://openalex.org/W3181252431","https://openalex.org/W4286223290","https://openalex.org/W4289821942","https://openalex.org/W4310645210","https://openalex.org/W4312910992","https://openalex.org/W4386076493"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,7,123,162],"development":[2],"of":[3,9,93,164],"medical":[4,31,62,95,165],"artificial":[5],"intelligence,":[6],"application":[8,163],"multimodal":[10,103],"data":[11,100],"fusion":[12],"in":[13,142,147],"disease":[14,23,56,144,154],"prediction":[15,24,46,58,110],"has":[16],"received":[17],"increasing":[18],"attention.":[19],"However,":[20],"most":[21],"existing":[22],"methods":[25],"rely":[26],"on":[27],"single-modality":[28],"data?such":[29],"as":[30],"imaging":[32,63],"or":[33],"clinical":[34,65,99,168],"text?which":[35],"makes":[36],"it":[37],"difficult":[38],"to":[39,73,84],"fully":[40],"exploit":[41],"cross-modal":[42,86,117],"associations,":[43],"thereby":[44,132],"limiting":[45],"accuracy.":[47,135],"To":[48],"address":[49],"this":[50],"limitation,":[51],"we":[52],"construct":[53],"a":[54,70,94,98,102,108],"chest":[55,143],"risk":[57],"model":[59,68,124],"that":[60],"integrates":[61],"and":[64,77,80,107,116,130,151],"text.":[66],"The":[67,89,136],"adopts":[69],"dual-tower":[71],"architecture":[72],"independently":[74],"encode":[75],"image":[76,96,129],"text":[78],"features":[79],"employs":[81],"contrastive":[82,104],"learning":[83,105],"optimize":[85],"semantic":[87,126],"alignment.":[88],"overall":[90],"framework":[91],"consists":[92],"encoder,":[97,101],"module,":[106],"fusion-based":[109],"module.":[111],"By":[112],"combining":[113],"intra-modal":[114],"self-attention":[115],"attention":[118,121],"through":[119],"bidirectional":[120],"interaction,":[122],"enhances":[125],"consistency":[127],"between":[128],"text,":[131],"improving":[133],"classification":[134],"proposed":[137],"method":[138],"demonstrates":[139],"outstanding":[140],"performance":[141],"prediction,":[145],"particularly":[146],"detecting":[148],"subtle":[149],"lesions":[150],"assessing":[152],"multi-label":[153],"risks.":[155],"This":[156],"study":[157],"offers":[158],"new":[159],"insights":[160],"into":[161],"AI":[166],"for":[167],"decision":[169],"support.":[170]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}
