{"id":"https://openalex.org/W4401750730","doi":"https://doi.org/10.1109/isbi56570.2024.10635819","title":"Mitigating Racial Bias in Chest X-Ray Disease Diagnosis Via Disentanglement Learning","display_name":"Mitigating Racial Bias in Chest X-Ray Disease Diagnosis Via Disentanglement Learning","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401750730","doi":"https://doi.org/10.1109/isbi56570.2024.10635819"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635819","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635819","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","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/A5113342716","display_name":"Xinwei Lai","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinwei Lai","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100347070","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0002-1819-294X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100679241","display_name":"Jie Li","orcid":"https://orcid.org/0000-0002-4974-6116"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021681612","display_name":"Zhusi Zhong","orcid":"https://orcid.org/0009-0008-5371-1443"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhusi Zhong","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068792201","display_name":"Xin Yang","orcid":"https://orcid.org/0000-0003-2281-9079"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yang","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113342716"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.4295,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.64852963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11894","display_name":"Radiology practices and education","score":0.9815999865531921,"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/T11894","display_name":"Radiology practices and education","score":0.9815999865531921,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9789999723434448,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9696999788284302,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5085948705673218},{"id":"https://openalex.org/keywords/x-ray","display_name":"X-ray","score":0.43318280577659607},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2337755262851715},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.17273598909378052}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5085948705673218},{"id":"https://openalex.org/C2779328170","wikidata":"https://www.wikidata.org/wiki/Q34777","display_name":"X-ray","level":2,"score":0.43318280577659607},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2337755262851715},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.17273598909378052}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635819","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635819","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7599999904632568,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2145810626","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2803187616","https://openalex.org/W2886283492","https://openalex.org/W2991229128","https://openalex.org/W2995225687","https://openalex.org/W3092398080","https://openalex.org/W3098528040","https://openalex.org/W3179023856","https://openalex.org/W3181414820","https://openalex.org/W4221159532","https://openalex.org/W4280519765","https://openalex.org/W4292958735","https://openalex.org/W4293004928","https://openalex.org/W4293004949","https://openalex.org/W4309984797","https://openalex.org/W4383377291","https://openalex.org/W6751420435","https://openalex.org/W6784706293","https://openalex.org/W6804364296"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"The":[0],"application":[1,51],"of":[2,44,52,62],"artificial":[3],"intelligence":[4],"(AI)":[5],"has":[6],"achieved":[7],"remarkable":[8],"advancements":[9],"in":[10,38,99],"medical":[11],"imaging.":[12],"However,":[13],"due":[14],"to":[15,29,58,70],"unbalanced":[16],"distribution,":[17],"most":[18],"existing":[19],"diagnoses":[20],"suffer":[21],"from":[22,79],"racial":[23,36,80,97,105],"bias,":[24],"where":[25],"diagnosis":[26,45],"model":[27,69,90],"tends":[28],"achieve":[30],"different":[31],"performance":[32,39],"among":[33],"races.":[34],"This":[35],"bias":[37],"seriously":[40],"affects":[41],"the":[42,50,60,88,93],"credibility":[43],"model,":[46],"and":[47,72,102],"eventually":[48],"hinders":[49],"new":[53],"technologies.":[54],"In":[55],"this":[56],"work,":[57],"address":[59],"issue":[61],"unfairness,":[63],"we":[64],"propose":[65],"a":[66],"novel":[67],"debias":[68],"disentangle":[71],"utilize":[73],"disease":[74,83],"features":[75,81],"that":[76,87],"are":[77,109],"apart":[78],"for":[82],"diagnosis.":[84],"Results":[85],"show":[86],"proposed":[89],"could":[91],"reduce":[92],"unfairness":[94],"caused":[95],"by":[96],"factors":[98],"imbalanced":[100],"data,":[101],"significantly":[103],"mitigate":[104],"bias.":[106],"Source":[107],"codes":[108],"available":[110],"at":[111],"Github<sup":[112],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[113],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
