{"id":"https://openalex.org/W4406495577","doi":"https://doi.org/10.1109/bigdata62323.2024.10825950","title":"Enhancing Fairness in Medical Image Classification: A Comparative Study of Convolutional Neural Networks and Adversarial Learning","display_name":"Enhancing Fairness in Medical Image Classification: A Comparative Study of Convolutional Neural Networks and Adversarial Learning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406495577","doi":"https://doi.org/10.1109/bigdata62323.2024.10825950"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5115913835","display_name":"Gauri Kshettry","orcid":null},"institutions":[{"id":"https://openalex.org/I60008374","display_name":"Middlesex County College","ror":"https://ror.org/03868wc59","country_code":"US","type":"education","lineage":["https://openalex.org/I60008374"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gauri Kshettry","raw_affiliation_strings":["Edison High School STEM Academy,Edison,New Jersey,USA"],"affiliations":[{"raw_affiliation_string":"Edison High School STEM Academy,Edison,New Jersey,USA","institution_ids":["https://openalex.org/I60008374"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034336322","display_name":"Twisha S Patel","orcid":null},"institutions":[{"id":"https://openalex.org/I60008374","display_name":"Middlesex County College","ror":"https://ror.org/03868wc59","country_code":"US","type":"education","lineage":["https://openalex.org/I60008374"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Twisha Patel","raw_affiliation_strings":["Edison High School STEM Academy,Edison,New Jersey,USA"],"affiliations":[{"raw_affiliation_string":"Edison High School STEM Academy,Edison,New Jersey,USA","institution_ids":["https://openalex.org/I60008374"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115913835"],"corresponding_institution_ids":["https://openalex.org/I60008374"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19574605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7386","last_page":"7390"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9968000054359436,"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"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9848999977111816,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.978600025177002,"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/adversarial-system","display_name":"Adversarial system","score":0.8366988301277161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7712315917015076},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.755182147026062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.678703248500824},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5612671971321106},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5196429491043091},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4550924301147461},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4535660445690155},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42057380080223083},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41439610719680786}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8366988301277161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712315917015076},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.755182147026062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.678703248500824},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5612671971321106},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5196429491043091},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4550924301147461},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4535660445690155},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42057380080223083},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41439610719680786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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":23,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2516809705","https://openalex.org/W2608702473","https://openalex.org/W2767106145","https://openalex.org/W2784397426","https://openalex.org/W2897154134","https://openalex.org/W2919115771","https://openalex.org/W2963116854","https://openalex.org/W2981869278","https://openalex.org/W2982127434","https://openalex.org/W4241857777","https://openalex.org/W4253763531","https://openalex.org/W4291414590","https://openalex.org/W4293846201","https://openalex.org/W4300273322","https://openalex.org/W6640425456","https://openalex.org/W6737947904","https://openalex.org/W6745643699","https://openalex.org/W6748256130","https://openalex.org/W6748382702","https://openalex.org/W6761100157","https://openalex.org/W6765285020","https://openalex.org/W6771898459"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W2911497689","https://openalex.org/W2952813363","https://openalex.org/W2963346891","https://openalex.org/W4360783045","https://openalex.org/W2770149305","https://openalex.org/W2795259429"],"abstract_inverted_index":{"Machine":[0],"learning":[1,25,49,75,115,136,172,178],"models":[2,15,179],"have":[3],"become":[4],"increasingly":[5],"prevalent":[6],"in":[7,34,116,153,180],"medical":[8],"diagnosis":[9],"and":[10,31,64,95,101,141,159,188],"healthcare":[11,35,181],"applications,":[12],"yet":[13],"these":[14,105],"often":[16],"exhibit":[17,128],"significant":[18],"biases.":[19,164],"This":[20,165],"study":[21,166],"investigates":[22],"whether":[23],"adversarial":[24,48,74,114,135,147,171],"can":[26],"effectively":[27],"mitigate":[28],"such":[29,71,92],"biases":[30],"improve":[32],"fairness":[33,190],"models.":[36],"We":[37],"compare":[38],"a":[39,78],"standard":[40,151],"convolutional":[41],"neural":[42],"network":[43],"(CNN)":[44],"model":[45,80,99,139,148],"with":[46],"an":[47],"approach":[50],"on":[51,89],"two":[52],"datasets:":[53],"the":[54,65,84,111,126,146,150,168],"Diverse":[55],"Dermatology":[56],"Images":[57],"Dataset":[58],"(DDI),":[59],"which":[60,69],"includes":[61],"demographic":[62],"annotations,":[63],"MNIST:":[66],"HAM10000":[67],"dataset,":[68],"lacks":[70],"annotations.":[72],"The":[73],"framework":[76],"incorporates":[77],"secondary":[79],"designed":[81],"to":[82,109,173],"challenge":[83],"primary":[85],"model\u2019s":[86],"predictions":[87],"based":[88],"sensitive":[90],"attributes":[91],"as":[93],"race":[94],"gender.":[96],"By":[97],"evaluating":[98],"performance":[100,140],"bias":[102,185],"reduction":[103],"across":[104,133],"datasets,":[106,134,155],"we":[107],"aim":[108],"determine":[110],"efficacy":[112],"of":[113,131,170],"promoting":[117],"more":[118,175,183],"equitable":[119,176],"outcomes.":[120],"Our":[121],"experiments":[122],"reveal":[123],"that":[124],"while":[125],"CNNs":[127],"varying":[129],"levels":[130],"accuracy":[132,158],"significantly":[137],"improves":[138],"reduces":[142],"racial":[143],"bias.":[144],"Specifically,":[145],"outperforms":[149],"CNN":[152],"both":[154],"achieving":[156],"higher":[157],"demonstrating":[160],"enhanced":[161],"robustness":[162],"against":[163],"underscores":[167],"potential":[169],"foster":[174],"machine":[177],"for":[182],"optimized":[184],"mitigation":[186],"techniques":[187],"comprehensive":[189],"evaluation.":[191]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
