{"id":"https://openalex.org/W3044699037","doi":"https://doi.org/10.1109/iwssip48289.2020.9145037","title":"DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays","display_name":"DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3044699037","doi":"https://doi.org/10.1109/iwssip48289.2020.9145037","mag":"3044699037"},"language":"en","primary_location":{"id":"doi:10.1109/iwssip48289.2020.9145037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwssip48289.2020.9145037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","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/A5089205519","display_name":"Vin\u00edcius Teixeira","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Vinicius Teixeira","raw_affiliation_strings":["Institute of Computing, University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079254324","display_name":"Leod\u00e9cio Braz da Silva Segundo","orcid":"https://orcid.org/0000-0002-9326-4122"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leodecio Braz","raw_affiliation_strings":["Institute of Computing, University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065725754","display_name":"H\u00e9lio Pedrini","orcid":"https://orcid.org/0000-0003-0125-630X"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Helio Pedrini","raw_affiliation_strings":["Institute of Computing, University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042140271","display_name":"Zanoni Dias","orcid":"https://orcid.org/0000-0003-3333-6822"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Zanoni Dias","raw_affiliation_strings":["Institute of Computing, University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5089205519"],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":0.9912,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.7923425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"69","last_page":"74"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9836000204086304,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9714999794960022,"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/discriminative-model","display_name":"Discriminative model","score":0.7296155691146851},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6806219816207886},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.662845253944397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6622081398963928},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6144850254058838},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.5762930512428284},{"id":"https://openalex.org/keywords/thorax","display_name":"Thorax (insect anatomy)","score":0.5494890213012695},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5289820432662964},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4537626802921295},{"id":"https://openalex.org/keywords/thoracic-diseases","display_name":"Thoracic diseases","score":0.4286855459213257},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.41038885712623596},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3698188066482544},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3519037365913391},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2254180908203125}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7296155691146851},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6806219816207886},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.662845253944397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6622081398963928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6144850254058838},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.5762930512428284},{"id":"https://openalex.org/C97834683","wikidata":"https://www.wikidata.org/wiki/Q942508","display_name":"Thorax (insect anatomy)","level":2,"score":0.5494890213012695},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5289820432662964},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4537626802921295},{"id":"https://openalex.org/C2909566329","wikidata":"https://www.wikidata.org/wiki/Q994554","display_name":"Thoracic diseases","level":2,"score":0.4286855459213257},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.41038885712623596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3698188066482544},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3519037365913391},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2254180908203125},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwssip48289.2020.9145037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwssip48289.2020.9145037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W2063878321","https://openalex.org/W2097117768","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2493683088","https://openalex.org/W2560892452","https://openalex.org/W2611650229","https://openalex.org/W2752782242","https://openalex.org/W2765414121","https://openalex.org/W2776460497","https://openalex.org/W2885373832","https://openalex.org/W2886327376","https://openalex.org/W2897806204","https://openalex.org/W2908899327","https://openalex.org/W2945689170","https://openalex.org/W2945946775","https://openalex.org/W2955805844","https://openalex.org/W2956897601","https://openalex.org/W2962884052","https://openalex.org/W2963446712","https://openalex.org/W2963606198","https://openalex.org/W2963942157","https://openalex.org/W2968607768","https://openalex.org/W2988214655","https://openalex.org/W2989100122","https://openalex.org/W2995323791","https://openalex.org/W2997225633","https://openalex.org/W2998180625","https://openalex.org/W3101156210","https://openalex.org/W4300485340","https://openalex.org/W6684191040","https://openalex.org/W6730849170","https://openalex.org/W6755368048","https://openalex.org/W6765384881"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2129933262","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W2964843961","https://openalex.org/W4291271001"],"abstract_inverted_index":{"The":[0,91,141],"chest":[1,89],"radiography":[2],"is":[3,126],"one":[4],"of":[5,13,30,47,59,84,94,115,133,157,172,182],"the":[6,45,57,65,82,104,119,130,134,153,158,175,180,187],"most":[7],"accessible":[8],"radiological":[9],"examinations":[10],"for":[11,81],"diagnosis":[12],"lung":[14],"and":[15,100,168],"heart":[16],"diseases.":[17],"Deep":[18],"learning":[19,150],"techniques":[20],"have":[21],"been":[22,145],"increasingly":[23],"used":[24],"to":[25,55,64,102,128,186],"provide":[26],"more":[27,112],"accurate":[28],"detection":[29],"thorax":[31,86],"lesions":[32],"on":[33,88],"Chest":[34],"X-Ray":[35],"(CXR)":[36],"images.":[37],"However,":[38],"we":[39,42,71],"observe":[40],"that":[41,165],"can":[43],"use":[44],"complementarity":[46],"dual":[48,75],"asymmetric":[49,96],"deep":[50,149],"convolutional":[51],"neural":[52],"networks":[53],"(DCNNs)":[54],"improve":[56],"ability":[58],"CXR":[60],"image":[61],"classification":[62,83],"compared":[63,185],"single":[66],"network.":[67],"In":[68],"this":[69],"paper,":[70],"propose":[72],"a":[73,107,123,138],"novel":[74],"lesion":[76],"attention":[77,97],"network":[78],"named":[79],"DuaLAnet":[80,92,143,166,183],"14":[85],"diseases":[87],"radiography.":[90],"consists":[93],"two":[95],"networks,":[98],"DenseNet-169":[99],"ResNet-152,":[101],"integrate":[103,129],"advantages":[105],"into":[106,137],"wider":[108],"architecture,":[109],"thus":[110],"extracting":[111],"discriminative":[113],"features":[114],"different":[116],"abnormalities":[117],"from":[118],"raw":[120],"CXRs.":[121],"Moreover,":[122],"training":[124],"strategy":[125],"designed":[127],"loss":[131],"contribution":[132],"involved":[135],"classifiers":[136],"unified":[139],"loss.":[140],"proposed":[142],"has":[144],"evaluated":[146],"against":[147],"eight":[148],"models":[151],"using":[152],"patient-wise":[154],"official":[155],"split":[156],"ChestX-ray14":[159],"dataset":[160],"[1].":[161],"Our":[162],"results":[163],"show":[164],"achieves":[167],"average":[169],"per-class":[170],"AUC":[171],"0.820":[173],"in":[174],"experiments,":[176],"which":[177],"clearly":[178],"substantiate":[179],"effectiveness":[181],"when":[184],"state-of-the-art":[188],"baselines.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
