{"id":"https://openalex.org/W4401072347","doi":"https://doi.org/10.1109/memea60663.2024.10596771","title":"CTGAN in Augmentation of Radiomics Features Classification from Narrow Band Imaging for Laryngeal Cancer","display_name":"CTGAN in Augmentation of Radiomics Features Classification from Narrow Band Imaging for Laryngeal Cancer","publication_year":2024,"publication_date":"2024-06-26","ids":{"openalex":"https://openalex.org/W4401072347","doi":"https://doi.org/10.1109/memea60663.2024.10596771"},"language":"en","primary_location":{"id":"doi:10.1109/memea60663.2024.10596771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea60663.2024.10596771","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 Medical Measurements and Applications (MeMeA)","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/A5100438288","display_name":"Haiyang Wang","orcid":"https://orcid.org/0000-0003-2273-2686"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Haiyang WANG","raw_affiliation_strings":["Polytechnic University of Milan,Department of Electrical, Information and Bioengineering,Milan,Italy"],"affiliations":[{"raw_affiliation_string":"Polytechnic University of Milan,Department of Electrical, Information and Bioengineering,Milan,Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066168605","display_name":"Luca Mainardi","orcid":"https://orcid.org/0000-0002-6276-6314"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Mainardi","raw_affiliation_strings":["Polytechnic University of Milan,Department of Electrical, Information and Bioengineering,Milan,Italy"],"affiliations":[{"raw_affiliation_string":"Polytechnic University of Milan,Department of Electrical, Information and Bioengineering,Milan,Italy","institution_ids":["https://openalex.org/I93860229"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100438288"],"corresponding_institution_ids":["https://openalex.org/I93860229"],"apc_list":null,"apc_paid":null,"fwci":1.5602,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83066211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9998000264167786,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9998000264167786,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/radiomics","display_name":"Radiomics","score":0.881493091583252},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.5236197113990784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4522772431373596},{"id":"https://openalex.org/keywords/narrow-band-imaging","display_name":"Narrow-band imaging","score":0.4443286061286926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3840387463569641},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.28501635789871216},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.24363437294960022},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.06891211867332458},{"id":"https://openalex.org/keywords/endoscopy","display_name":"Endoscopy","score":0.05502820014953613}],"concepts":[{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.881493091583252},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.5236197113990784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4522772431373596},{"id":"https://openalex.org/C2781399487","wikidata":"https://www.wikidata.org/wiki/Q6966306","display_name":"Narrow-band imaging","level":3,"score":0.4443286061286926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3840387463569641},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.28501635789871216},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.24363437294960022},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.06891211867332458},{"id":"https://openalex.org/C2778451229","wikidata":"https://www.wikidata.org/wiki/Q212809","display_name":"Endoscopy","level":2,"score":0.05502820014953613}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/memea60663.2024.10596771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea60663.2024.10596771","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 Medical Measurements and Applications (MeMeA)","raw_type":"proceedings-article"},{"id":"pmh:oai:re.public.polimi.it:11311/1276757","is_oa":false,"landing_page_url":"https://hdl.handle.net/11311/1276757","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1581868640","https://openalex.org/W2099057450","https://openalex.org/W2119821739","https://openalex.org/W2767128594","https://openalex.org/W2775496002","https://openalex.org/W2911964244","https://openalex.org/W2963073614","https://openalex.org/W3004661948","https://openalex.org/W3048802680","https://openalex.org/W3080580366","https://openalex.org/W3101502877","https://openalex.org/W3120254195","https://openalex.org/W3158157866","https://openalex.org/W3197438232","https://openalex.org/W4256561644","https://openalex.org/W4283831124","https://openalex.org/W4288296172","https://openalex.org/W4297833479","https://openalex.org/W4309028162","https://openalex.org/W4312114404","https://openalex.org/W4319754535","https://openalex.org/W4386075954","https://openalex.org/W4391422801","https://openalex.org/W4392135599","https://openalex.org/W6674887261","https://openalex.org/W6765451912"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3000891326","https://openalex.org/W4205100762","https://openalex.org/W2118176056","https://openalex.org/W2734724112","https://openalex.org/W2582997534","https://openalex.org/W3009210156","https://openalex.org/W4388577230"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"(AI)":[2],"holds":[3],"immense":[4],"promise":[5],"in":[6,11,26,53,95,107,211,261],"revolutionizing":[7],"biomedical":[8,54],"research,":[9],"particularly":[10],"the":[12,22,27,77,93,129,135,138,147,165,180,184,195,220,224,262],"field":[13,55],"of":[14,24,29,51,67,97,118,186,200,223],"early":[15,35],"medical":[16,257],"assistance":[17],"analysis.":[18],"This":[19,218],"paper":[20,244],"explores":[21],"application":[23],"AI":[25,52],"context":[28],"laryngeal":[30,110,190],"cancer,":[31],"a":[32,108,154,198,246],"disease":[33],"where":[34],"screening,":[36],"accurate":[37],"diagnosis,":[38],"effective":[39],"management,":[40],"and":[41,82,146,149,169,182,234,248],"favorable":[42],"prognosis":[43],"are":[44],"crucial":[45],"for":[46,189,256],"patient":[47],"outcomes.":[48],"The":[49,64,124,161],"implementation":[50],"practice":[56],"always":[57],"faces":[58],"challenges":[59],"due":[60],"to":[61,70,113,216],"data":[62,68,119,131,163,167,172,188,239,254],"scarcity.":[63],"limited":[65],"availability":[66],"leads":[69],"less":[71],"satisfactory":[72],"testing":[73,230],"results.":[74],"Even":[75],"if":[76],"methods":[78],"like":[79],"geometric":[80],"transformation":[81,84],"photometric":[83],"have":[85],"been":[86],"applied,":[87],"it":[88],"does":[89],"not":[90],"still":[91],"enlarge":[92],"diversity":[94,181],"nature":[96],"data.":[98],"Here,":[99],"we":[100],"investigated":[101],"CTGAN":[102],"on":[103,227,251],"tabular":[104,252],"radiomics":[105,253],"features":[106],"public":[109],"cancer":[111,191],"dataset":[112,231],"check":[114],"how":[115,237],"various":[116],"amount":[117,185],"augmentation":[120,255],"affects":[121],"classifier's":[122],"performance.":[123],"results":[125],"were":[126],"assessed":[127],"by":[128],"synthetic":[130,162,171,196],"reports":[132],"which":[133],"captures":[134],"similarity":[136],"with":[137,153],"columns":[139,152],"shapes":[140],"score":[141,157],"(median":[142],"value":[143,159],"71.23":[144],"%)":[145],"trend":[148],"correction":[150],"across":[151,202],"column":[155],"pair":[156],"median":[158],"90.30%.":[160],"respect":[164],"original":[166],"structure(100%)":[168],"overall":[170],"validity":[173],"is":[174],"above":[175],"81":[176],"%.":[177],"It":[178],"enhances":[179],"increase":[183],"training":[187],"detection.":[192],"After":[193],"assessing":[194],"report,":[197],"comparison":[199],"performances":[201],"different":[203],"classifiers":[204],"was":[205],"followed.":[206],"Result":[207],"shows":[208],"an":[209,228],"increases":[210],"accuracy":[212],"from":[213],"5":[214],"%":[215],"10%.":[217],"proves":[219],"positive":[221,247],"performance":[222],"classifying":[225],"improvement":[226],"independent":[229],"(real":[232],"data)":[233],"provides":[235,245],"clues":[236],"much":[238],"should":[240],"be":[241],"synthesized.":[242],"Our":[243],"meaningful":[249],"reference":[250],"intelligent":[258],"diagnosis":[259],"design":[260],"future.":[263]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
