{"id":"https://openalex.org/W3137876632","doi":"https://doi.org/10.1109/bigdata50022.2020.9377793","title":"Not All Areas Are Equal: Detecting Thoracic Disease With ChestWNet","display_name":"Not All Areas Are Equal: Detecting Thoracic Disease With ChestWNet","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137876632","doi":"https://doi.org/10.1109/bigdata50022.2020.9377793","mag":"3137876632"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9377793","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377793","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5033154908","display_name":"Yang Zhou","orcid":"https://orcid.org/0000-0001-5474-9605"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhou Yang","raw_affiliation_strings":["Department of Statistics, George Washington University"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, George Washington University","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083206555","display_name":"Zhenhe Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenhe Pan","raw_affiliation_strings":["Department of Computer Science, Texas Tech University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Texas Tech University","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016451064","display_name":"Sisheng Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sisheng Liang","raw_affiliation_strings":["Department of Computer Science, Texas Tech University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Texas Tech University","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101706801","display_name":"Fang Jin","orcid":"https://orcid.org/0000-0002-6606-5232"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fang Jin","raw_affiliation_strings":["Department of Statistics, George Washington University"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, George Washington University","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033154908"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25934714,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"3447","last_page":"3452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9745000004768372,"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/T10862","display_name":"AI in cancer detection","score":0.970300018787384,"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/weighting","display_name":"Weighting","score":0.8702728152275085},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.746710479259491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7025461792945862},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6792709827423096},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6780538558959961},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6118603944778442},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5476384162902832},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5057179927825928},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5011489391326904},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.43673816323280334},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42509356141090393},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.359372079372406},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09531038999557495},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.07160744071006775},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.06579738855361938}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.8702728152275085},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746710479259491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7025461792945862},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6792709827423096},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6780538558959961},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6118603944778442},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5476384162902832},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5057179927825928},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5011489391326904},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.43673816323280334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42509356141090393},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.359372079372406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09531038999557495},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.07160744071006775},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.06579738855361938},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9377793","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377793","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2001032963","https://openalex.org/W2096451472","https://openalex.org/W2108598243","https://openalex.org/W2253429366","https://openalex.org/W2295107390","https://openalex.org/W2611650229","https://openalex.org/W2765312638","https://openalex.org/W2770241596","https://openalex.org/W2772246530","https://openalex.org/W2962708065","https://openalex.org/W2962858109","https://openalex.org/W2962975664","https://openalex.org/W2963446712","https://openalex.org/W2963967185","https://openalex.org/W3101156210","https://openalex.org/W4205623122","https://openalex.org/W4295608163","https://openalex.org/W4300485340","https://openalex.org/W6674472854","https://openalex.org/W6745119210","https://openalex.org/W6746693533"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W3183901164","https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W2951211570","https://openalex.org/W3192840557","https://openalex.org/W3176438653","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Automating":[0],"pneumonia":[1],"diagnosis":[2],"from":[3,24,58],"X-ray":[4,100],"images":[5],"could":[6],"significantly":[7,118],"improve":[8],"patient":[9],"diagnosing":[10],"outcomes.":[11],"A":[12],"major":[13],"challenge":[14],"is":[15],"that":[16,46,116],"disease":[17],"information":[18],"(features)":[19],"must":[20],"be":[21,127],"extracted":[22],"directly":[23],"the":[25,70,91,106],"image":[26,92],"backgrounds.":[27],"Motivated":[28],"by":[29],"recent":[30],"advances":[31],"in":[32,95,110,136],"Convolutional":[33],"Neural":[34],"Network":[35],"(CNN),":[36],"we":[37],"propose":[38],"a":[39],"hierarchical":[40,87],"weighting":[41,62,88],"deep":[42],"learning":[43,51,77,85],"model,":[44],"ChestWNet,":[45],"combines":[47],"DenseNet":[48],"and":[49,54,76,82,90,124],"transfer":[50],"to":[52,66,104,129],"detect":[53],"localize":[55],"thoracic":[56],"diseases":[57],"chest":[59],"x-rays.":[60],"Hierarchical":[61],"networks":[63,89],"are":[64,102],"designed":[65],"assign":[67],"scores":[68],"reflecting":[69],"importance":[71],"of":[72],"specific":[73],"pixels":[74],"(regions),":[75],"weights":[78],"at":[79],"pixel-,":[80],"region-,":[81],"image-levels,":[83],"jointly":[84],"these":[86,111],"classification":[93],"network":[94],"an":[96],"end-to-end":[97],"manner.":[98],"Chest":[99],"datasets":[101],"customized":[103],"solve":[105],"unbalancing":[107],"label":[108],"problem":[109],"datasets.":[112],"Extensive":[113],"experiments":[114],"show":[115],"ChestWNet":[117],"outperforms":[119],"other":[120],"established":[121],"prediction":[122],"methods,":[123],"can":[125],"also":[126],"applied":[128],"similar":[130],"scenarios":[131],"with":[132],"fixed":[133],"point-of-interest":[134],"regions":[135],"images.":[137]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
