{"id":"https://openalex.org/W4400224475","doi":"https://doi.org/10.1145/3649153.3649216","title":"CvTGNet: A Novel Framework for Chest X-Ray Multi-label Classification","display_name":"CvTGNet: A Novel Framework for Chest X-Ray Multi-label Classification","publication_year":2024,"publication_date":"2024-05-07","ids":{"openalex":"https://openalex.org/W4400224475","doi":"https://doi.org/10.1145/3649153.3649216"},"language":"en","primary_location":{"id":"doi:10.1145/3649153.3649216","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3649153.3649216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM International Conference on Computing Frontiers","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/A5063621364","display_name":"Yu Lu","orcid":"https://orcid.org/0000-0002-7799-9794"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Lu","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-7799-9794","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014359771","display_name":"Yating Hu","orcid":"https://orcid.org/0009-0002-3508-2029"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yating Hu","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0002-3508-2029","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008680227","display_name":"Leya Li","orcid":"https://orcid.org/0009-0001-6533-5034"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leya Li","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0001-6533-5034","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029492607","display_name":"Zhanpeng Xu","orcid":"https://orcid.org/0009-0007-6365-1598"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanpeng Xu","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0007-6365-1598","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hongwei Liu","orcid":"https://orcid.org/0009-0006-5196-7424"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Liu","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0006-5196-7424","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018841882","display_name":"Huanwen Liang","orcid":"https://orcid.org/0000-0002-3412-8534"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanwen Liang","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3412-8534","affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086110173","display_name":"Xianghua Fu","orcid":"https://orcid.org/0009-0008-7222-5264"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghua Fu","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0008-7222-5264","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"12","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9908000230789185,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9883999824523926,"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/computer-science","display_name":"Computer science","score":0.6507425904273987},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.6390693187713623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3917008638381958}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507425904273987},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.6390693187713623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3917008638381958}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3649153.3649216","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3649153.3649216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM International Conference on Computing Frontiers","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":17,"referenced_works":["https://openalex.org/W2096451472","https://openalex.org/W2250539671","https://openalex.org/W2611650229","https://openalex.org/W2962975664","https://openalex.org/W2963466845","https://openalex.org/W2963942157","https://openalex.org/W2964051675","https://openalex.org/W2989100348","https://openalex.org/W3002476946","https://openalex.org/W3101156210","https://openalex.org/W3110181119","https://openalex.org/W3120013481","https://openalex.org/W3128150850","https://openalex.org/W3167456680","https://openalex.org/W4214493665","https://openalex.org/W4214673031","https://openalex.org/W4321071737"],"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/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"The":[0,139],"accurate":[1,117],"diagnosis":[2],"of":[3,67,137,153,176,179,196],"multiple":[4,206],"thoracic":[5,89,207],"diseases":[6,208],"through":[7],"chest":[8],"X-ray":[9],"(CXR)":[10],"images":[11],"is":[12,59],"a":[13,40,69,186],"challenging":[14],"yet":[15],"crucial":[16],"task":[17],"in":[18,28,31,114,151,189,204,216],"the":[19,63,85,92,111,135,156,174,194],"medical":[20,218],"field.":[21],"Deep":[22],"learning":[23],"approaches":[24,150],"have":[25],"shown":[26],"promise":[27,215],"assisting":[29],"clinicians":[30,221],"this":[32,35],"endeavor.":[33],"In":[34],"paper,":[36],"we":[37,200],"introduce":[38],"CvTGNet,":[39],"novel":[41],"framework":[42,184],"that":[43,72,101,142],"leverages":[44],"Convolutional":[45,51,74],"Vision":[46],"Transformer":[47],"(CvT)":[48],"and":[49,78,131,198,227],"Graph":[50],"Network":[52],"(GCN)":[53],"to":[54,61,83,94,133,172,222],"enhance":[55],"CXR":[56,128,190,210],"diagnosis.":[57,191],"CvTGNet":[58,183],"designed":[60],"harness":[62],"super":[64],"generalization":[65],"capability":[66],"CvT,":[68],"hybrid":[70],"architecture":[71],"combines":[73],"Neural":[75],"Networks":[76],"(CNNs)":[77],"Transformers.":[79],"We":[80,121,167],"employ":[81],"GCN":[82],"explore":[84],"co-occurrence":[86],"relationships":[87],"among":[88],"diseases,":[90],"allowing":[91],"model":[93,113],"gain":[95],"insights":[96],"into":[97],"intricate":[98],"pathological":[99,119,165],"connections":[100],"might":[102],"be":[103],"overlooked":[104],"by":[105],"traditional":[106],"methods.":[107],"This":[108,212],"information":[109],"guides":[110],"CvT":[112,197],"making":[115],"more":[116,224],"multi-label":[118],"classifications.":[120],"conducted":[122,169],"extensive":[123],"experiments":[124,171],"on":[125],"two":[126],"prominent":[127],"datasets,":[129],"ChestX-Ray14":[130],"CheXpert,":[132],"evaluate":[134],"performance":[136],"CvTGNet.":[138,180],"results":[140],"demonstrate":[141],"our":[143,182],"proposed":[144],"method":[145],"consistently":[146],"outperforms":[147],"other":[148],"state-of-the-art":[149],"terms":[152],"Area":[154],"Under":[155],"Receiver":[157],"Operating":[158],"Characteristic":[159],"Curve":[160],"(AUC-ROC)":[161],"scores":[162],"for":[163],"multilabel":[164],"classification.":[166],"also":[168],"ablation":[170],"understand":[173],"contribution":[175],"each":[177],"component":[178],"Overall,":[181],"presents":[185],"significant":[187],"advancement":[188],"By":[192],"combining":[193],"strengths":[195],"CGN,":[199],"achieve":[201],"remarkable":[202],"accuracy":[203],"detecting":[205],"from":[209],"images.":[211],"approach":[213],"holds":[214],"enhancing":[217],"diagnosis,":[219],"enabling":[220],"make":[223],"informed":[225],"decisions":[226],"improving":[228],"patient":[229],"outcomes.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
