{"id":"https://openalex.org/W4321649186","doi":"https://doi.org/10.48550/arxiv.2302.11082","title":"BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition","display_name":"BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition","publication_year":2023,"publication_date":"2023-02-22","ids":{"openalex":"https://openalex.org/W4321649186","doi":"https://doi.org/10.48550/arxiv.2302.11082"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2302.11082","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.11082","pdf_url":"https://arxiv.org/pdf/2302.11082","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.11082","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100696242","display_name":"Guoli Wang","orcid":"https://orcid.org/0000-0001-9689-582X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Guoli","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100754146","display_name":"Pingping Wang","orcid":"https://orcid.org/0000-0003-3026-9038"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Pingping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085588064","display_name":"Jinyu Cong","orcid":"https://orcid.org/0000-0002-5435-5259"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cong, Jinyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024317728","display_name":"Kunmeng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Kunmeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5051864449","display_name":"Benzheng Wei","orcid":"https://orcid.org/0000-0001-9640-4947"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Benzheng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100696242"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9950000047683716,"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":0.9950000047683716,"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":0.9652000069618225,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9502999782562256,"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":0.6190726161003113},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6168607473373413},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.615632951259613},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5884302854537964},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5825875401496887},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5190966129302979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5182476043701172},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.474972665309906},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.448302686214447},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4423770010471344},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4314756393432617},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.25869250297546387}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6190726161003113},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6168607473373413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.615632951259613},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5884302854537964},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5825875401496887},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5190966129302979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5182476043701172},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.474972665309906},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.448302686214447},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4423770010471344},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4314756393432617},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25869250297546387},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2302.11082","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.11082","pdf_url":"https://arxiv.org/pdf/2302.11082","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2302.11082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.11082","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.11082","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.11082","pdf_url":"https://arxiv.org/pdf/2302.11082","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2282700838","display_name":null,"funder_award_id":"ZR2020QF043","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G2696157397","display_name":null,"funder_award_id":"ZR201","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G3497226477","display_name":null,"funder_award_id":"ZR2019","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G5011498858","display_name":null,"funder_award_id":"ZR2019ZD04","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5439878917","display_name":null,"funder_award_id":"ZR2020ZD4","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G5585983100","display_name":null,"funder_award_id":"61872225","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G591479136","display_name":null,"funder_award_id":"ZR2020","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G5976735303","display_name":null,"funder_award_id":"ZR2019ZD04","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G6058138561","display_name":null,"funder_award_id":", No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6066506773","display_name":null,"funder_award_id":"61872225","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G6289866512","display_name":null,"funder_award_id":"No. ZR2020QF043","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G7080924630","display_name":null,"funder_award_id":"ZR2020KF013","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G7387936317","display_name":null,"funder_award_id":"ZR2020ZD44","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G8486475920","display_name":null,"funder_award_id":"No. ZR2020ZD44","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G857842255","display_name":null,"funder_award_id":"No. ZR2020KF013","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G8735861359","display_name":null,"funder_award_id":"2019-173","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4321649186.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W3036264823","https://openalex.org/W4388405611","https://openalex.org/W3206528106","https://openalex.org/W2619127353","https://openalex.org/W2123605750","https://openalex.org/W2912814903"],"abstract_inverted_index":{"Multi-label":[0],"chest":[1,224],"X-ray":[2,225],"(CXR)":[3],"recognition":[4,38,212,226],"involves":[5],"simultaneously":[6],"diagnosing":[7],"and":[8,48,56,72,78,114,145,162,182,199,204,227],"identifying":[9],"multiple":[10,143],"labels":[11,17,33,144],"for":[12,150,193],"different":[13],"pathologies.":[14],"Since":[15],"pathological":[16,32],"have":[18,168],"rich":[19],"information":[20],"about":[21],"their":[22],"relationship":[23,110,141],"to":[24,36,51,136],"each":[25],"other,":[26],"modeling":[27],"the":[28,76,122,138,157,165,170,173,189,194,211],"co-occurrence":[29,61,140],"dependencies":[30],"between":[31,63,142],"is":[34,96],"essential":[35],"improve":[37,210],"performance.":[39,232],"However,":[40],"previous":[41],"methods":[42,67],"rely":[43],"on":[44],"state":[45],"variable":[46],"coding":[47],"attention":[49],"mechanisms-oriented":[50],"model":[52,95,99,137,185,217],"local":[53],"label":[54,73],"information,":[55],"lack":[57],"learning":[58,151],"of":[59,102,172,214],"global":[60,139],"relationships":[62],"labels.":[64],"Furthermore,":[65],"these":[66,86],"roughly":[68],"integrate":[69],"image":[70,126],"features":[71],"embedding,":[74],"ignoring":[75],"alignment":[77],"compactness":[79],"problems":[80],"in":[81,176,222],"cross-modal":[82,158],"vector":[83],"fusion.To":[84],"solve":[85],"problems,":[87],"a":[88,103,106,115,134],"Bi-modal":[89],"Bridged":[90],"Graph":[91,118],"Convolutional":[92],"Network":[93],"(BB-GCN)":[94],"proposed.":[97],"This":[98],"mainly":[100],"consists":[101],"backbone":[104,123],"module,":[105,113],"pathology":[107],"Label":[108],"Co-occurrence":[109],"Embedding":[111],"(LCE)":[112],"Transformer":[116],"Bridge":[117],"(TBG)":[119],"module.":[120],"Specifically,":[121],"module":[124,132,155],"obtains":[125],"visual":[127],"feature":[128],"representation.":[129],"The":[130,153],"LCE":[131,203],"utilizes":[133],"graph":[135,147],"employs":[146],"convolutional":[148],"networks":[149],"inference.":[152],"TBG":[154,205],"bridges":[156],"vectors":[159],"more":[160],"compactly":[161],"efficiently":[163],"through":[164],"GroupSum":[166],"method.We":[167],"evaluated":[169],"effectiveness":[171],"proposed":[174,202],"BB-GCN":[175],"two":[177],"large-scale":[178],"CXR":[179],"datasets":[180],"(ChestX-Ray14":[181],"CheXpert).":[183],"Our":[184,216],"achieved":[186],"state-of-the-art":[187],"performance:":[188],"mean":[190],"AUC":[191],"scores":[192],"14":[195],"pathologies":[196],"were":[197],"0.835":[198],"0.813,":[200],"respectively.The":[201],"modules":[206],"can":[207],"jointly":[208],"effectively":[209],"performance":[213],"BB-GCN.":[215],"also":[218],"achieves":[219],"satisfactory":[220],"results":[221],"multi-label":[223],"exhibits":[228],"highly":[229],"competitive":[230],"generalization":[231]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2023-02-24T00:00:00"}