{"id":"https://openalex.org/W7126034044","doi":"https://doi.org/10.1109/bibm66473.2025.11356707","title":"Generalized Nesterov-Boosted Adversarial Data Augmentation Framework for Multi-Label Chest X-Ray Image Classification","display_name":"Generalized Nesterov-Boosted Adversarial Data Augmentation Framework for Multi-Label Chest X-Ray Image Classification","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126034044","doi":"https://doi.org/10.1109/bibm66473.2025.11356707"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356707","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":"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/A5121590389","display_name":"Zhanbo Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhanbo Liang","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124256784","display_name":"Yuping Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuping Sun","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100391306","display_name":"Si Li","orcid":"https://orcid.org/0000-0001-5590-7759"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Si Li","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121590389"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73131921,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3811","last_page":"3816"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.7961999773979187,"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":0.7961999773979187,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0357000008225441,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.023399999365210533,"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/adversarial-system","display_name":"Adversarial system","score":0.8324000239372253},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.623199999332428},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.570900022983551},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5627999901771545},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5618000030517578},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42640000581741333},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3887999951839447}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8324000239372253},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6238999962806702},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.623199999332428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6129999756813049},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.570900022983551},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5627999901771545},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5618000030517578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40470001101493835},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27639999985694885},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356707","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":[{"id":"https://metadata.un.org/sdg/8","score":0.7077380418777466,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W2111316763","https://openalex.org/W2183341477","https://openalex.org/W2765793020","https://openalex.org/W2774644650","https://openalex.org/W2963466845","https://openalex.org/W2963542245","https://openalex.org/W2995225687","https://openalex.org/W3165691894","https://openalex.org/W4214673031","https://openalex.org/W4312535972","https://openalex.org/W4312648273","https://openalex.org/W4377252607","https://openalex.org/W4389010857","https://openalex.org/W4400188495"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"methods":[2,17],"have":[3],"shown":[4],"promising":[5],"results":[6],"in":[7,42],"multi-label":[8,59,167],"chest":[9],"X-ray":[10],"(CXR)":[11],"image":[12,61,169],"classification.":[13,62],"However,":[14],"most":[15],"existing":[16],"rely":[18],"on":[19,69,73,109,150],"large-scale":[20],"fully-annotated":[21],"datasets,":[22],"which":[23,125],"are":[24],"costly":[25],"and":[26,156],"laborious":[27],"to":[28,88,103],"obtain.":[29],"Therefore,":[30],"training":[31,136],"a":[32,39,50,79,121,138],"high-performance":[33],"model":[34,71,107,135],"with":[35],"limited":[36],"annotation":[37],"remains":[38],"significant":[40],"challenge":[41],"practice.":[43],"To":[44],"address":[45],"this":[46],"issue,":[47],"we":[48,64,77,97],"propose":[49,78],"Generalized":[51,80],"Nesterov-Boosted":[52],"Adversarial":[53],"Data":[54],"Augmentation":[55],"(GN-ADA)":[56],"framework":[57,165],"for":[58,134,166],"CXR":[60,153,168],"First,":[63],"generate":[65,89],"pseudo":[66],"labels":[67],"based":[68],"the":[70,110,117,144,159,162],"predictions":[72,108],"weakly-augmented":[74],"images.":[75],"Next,":[76],"Nesterov":[81],"Iterative":[82],"Fast":[83],"Gradient":[84],"Sign":[85],"Method":[86],"(GNI-FGSM)":[87],"effective":[90,131],"adversarial":[91,112,132],"examples":[92,133],"as":[93],"strongly-augmented":[94],"data.":[95],"Then,":[96],"introduce":[98],"an":[99],"adversarial-augmentation-based":[100],"consistency":[101],"regularization":[102],"perform":[104],"supervision":[105],"of":[106,128,161],"above":[111],"examples.":[113],"We":[114],"note":[115],"that":[116],"proposed":[118,163],"GNI-FGSM":[119],"is":[120,126],"higher-order":[122],"FGSM":[123],"variant,":[124],"capable":[127],"generating":[129],"more":[130],"within":[137],"constrained":[139],"time":[140],"frame,":[141],"thereby":[142],"improving":[143],"overall":[145],"classification":[146,170],"performance.":[147],"Extensive":[148],"experiments":[149],"two":[151],"large":[152],"datasets":[154],"(CheXpert":[155],"MIMIC-CXR)":[157],"demonstrate":[158],"effectiveness":[160],"GN-ADA":[164],"under":[171],"limited-annotation":[172],"scenario.":[173]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
