{"id":"https://openalex.org/W7126116744","doi":"https://doi.org/10.1109/bibm66473.2025.11356192","title":"Causal-Aware Swin Transformer with MAE Pretraining for Multi-Label Chest X-Ray Classification","display_name":"Causal-Aware Swin Transformer with MAE Pretraining for Multi-Label Chest X-Ray Classification","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126116744","doi":"https://doi.org/10.1109/bibm66473.2025.11356192"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356192","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/A5124218914","display_name":"Shiyu Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4210154361","display_name":"Shanghai Jian Qiao University","ror":"https://ror.org/04xdqtw10","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210154361"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyu Song","raw_affiliation_strings":["Shanghai Jianjing Culture Communication Co., Ltd.,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jianjing Culture Communication Co., Ltd.,Shanghai,China","institution_ids":["https://openalex.org/I4210154361"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013723764","display_name":"Chunyang Ye","orcid":"https://orcid.org/0000-0002-2177-8255"},"institutions":[{"id":"https://openalex.org/I142415962","display_name":"Beijing Institute of Education","ror":"https://ror.org/03aefdx31","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142415962"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyang Ye","raw_affiliation_strings":["Chuanzhi Education Group,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chuanzhi Education Group,Beijing,China","institution_ids":["https://openalex.org/I142415962"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100346343","display_name":"Yiming Li","orcid":"https://orcid.org/0000-0002-6107-5618"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":"4884","last_page":"4892"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.6930000185966492,"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":0.6930000185966492,"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":0.062199998646974564,"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":0.0568000003695488,"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/spurious-relationship","display_name":"Spurious relationship","score":0.6220999956130981},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5909000039100647},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.571399986743927},{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.5202000141143799},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45190000534057617},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3813999891281128},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.33649998903274536},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3361999988555908}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6348000168800354},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6220999956130981},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5909000039100647},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.571399986743927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5665000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5414000153541565},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.5202000141143799},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.2973000109195709},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356192","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":11,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2592929672","https://openalex.org/W2611650229","https://openalex.org/W2948579453","https://openalex.org/W3138516171","https://openalex.org/W4221163766","https://openalex.org/W4313156423","https://openalex.org/W4386798944","https://openalex.org/W4402727020","https://openalex.org/W4403791551","https://openalex.org/W4406271405"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"multi-label":[1,155],"classification":[2,117],"of":[3,21,69,122,139],"thoracic":[4,123],"diseases":[5],"from":[6,109],"chest":[7],"X-rays":[8],"continues":[9],"to":[10,14,81,86,106],"be":[11],"challenging":[12],"due":[13],"strong":[15],"inter-class":[16],"correlations":[17],"and":[18,75,153],"limited":[19,110],"generalization":[20],"traditional":[22],"convolutional":[23],"approaches.":[24],"In":[25,55],"this":[26],"work,":[27],"we":[28,57],"propose":[29],"a":[30,59,148],"Causal-Aware":[31],"Swin":[32,94],"Transformer":[33,95],"(CAST)":[34],"framework":[35,135],"that":[36,63],"integrates":[37],"causal":[38,67,141],"inference":[39],"components":[40],"with":[41,98,143],"Masked":[42],"Autoencoder":[43],"(MAE)":[44],"pretraining,":[45,100],"for":[46,151,157],"better":[47],"representation":[48],"learning":[49],"on":[50,83],"the":[51,66,79,87,103,137],"NIH":[52],"ChestX-ray14":[53],"dataset.":[54],"particular,":[56],"develop":[58],"Causal":[60],"Attention":[61],"module":[62],"explicitly":[64],"learns":[65],"contribution":[68],"latent":[70],"features":[71],"while":[72,89],"contrasting":[73],"original":[74],"counterfactual":[76],"representations,":[77],"allowing":[78],"network":[80],"focus":[82],"cues":[84],"related":[85],"disease":[88],"suppressing":[90],"spurious":[91],"correlations.":[92],"The":[93],"backbone,":[96],"initialized":[97],"MAE":[99],"also":[101],"enhances":[102],"model's":[104],"ability":[105],"learn":[107],"hierarchically":[108],"supervision.":[111],"Our":[112],"empirical":[113],"evidence":[114],"shows":[115],"improved":[116],"performance":[118],"in":[119],"14":[120],"categories":[121],"disease,":[124],"achieving":[125],"higher":[126],"mean":[127],"AUC":[128],"than":[129],"existing":[130],"state-of-the-art":[131],"methods.":[132],"This":[133],"proposed":[134],"highlights":[136],"effectiveness":[138],"combining":[140],"reasoning":[142],"transformer":[144],"based":[145],"methods":[146],"as":[147],"promising":[149],"approach":[150],"interpretable":[152],"robust":[154],"modeling":[156],"medical":[158],"image":[159],"classification.":[160]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}
