{"id":"https://openalex.org/W3199695236","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533851","title":"3D Multi-Branch Encoder-Decoder Networks with Attentional Feature Fusion for Pulmonary Nodule Detection in CT Scans","display_name":"3D Multi-Branch Encoder-Decoder Networks with Attentional Feature Fusion for Pulmonary Nodule Detection in CT Scans","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3199695236","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533851","mag":"3199695236"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5024304250","display_name":"Chenjiao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenjiao Zhang","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100709076","display_name":"Lulu Wang","orcid":"https://orcid.org/0000-0002-6796-2533"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lulu Wang","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061869604","display_name":"Xing Wu","orcid":"https://orcid.org/0000-0002-3352-6960"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Wu","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100676074","display_name":"Zhongshi He","orcid":"https://orcid.org/0009-0007-3682-0464"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongshi He","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024304250"],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":0.437,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65226112,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"79","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9994999766349792,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9911999702453613,"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":0.9822999835014343,"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/computer-science","display_name":"Computer science","score":0.731653094291687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6809384822845459},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6239736080169678},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.599909782409668},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5834400057792664},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5483189821243286},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5355007648468018},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.46110138297080994},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.42391887307167053},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3580051362514496}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.731653094291687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6809384822845459},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6239736080169678},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.599909782409668},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5834400057792664},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5483189821243286},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5355007648468018},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.46110138297080994},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.42391887307167053},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3580051362514496},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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":47,"referenced_works":["https://openalex.org/W130099911","https://openalex.org/W639708223","https://openalex.org/W1665214252","https://openalex.org/W1901129140","https://openalex.org/W1986649315","https://openalex.org/W2031827337","https://openalex.org/W2102605133","https://openalex.org/W2194775991","https://openalex.org/W2322371438","https://openalex.org/W2565639579","https://openalex.org/W2584017349","https://openalex.org/W2613718673","https://openalex.org/W2626997132","https://openalex.org/W2724169034","https://openalex.org/W2747257024","https://openalex.org/W2769848455","https://openalex.org/W2800755727","https://openalex.org/W2806695489","https://openalex.org/W2807106332","https://openalex.org/W2887808321","https://openalex.org/W2890179236","https://openalex.org/W2922509574","https://openalex.org/W2924892625","https://openalex.org/W2937068764","https://openalex.org/W2940711273","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2963420686","https://openalex.org/W2963516811","https://openalex.org/W2963777800","https://openalex.org/W2988452521","https://openalex.org/W3016719260","https://openalex.org/W3028412523","https://openalex.org/W3034552520","https://openalex.org/W3094897602","https://openalex.org/W3102190365","https://openalex.org/W3106250896","https://openalex.org/W4239908350","https://openalex.org/W6628973269","https://openalex.org/W6637242042","https://openalex.org/W6639824700","https://openalex.org/W6704278359","https://openalex.org/W6743731764","https://openalex.org/W6746260709","https://openalex.org/W6754130081","https://openalex.org/W6784088632","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3046682795"],"abstract_inverted_index":{"Pulmonary":[0],"nodule":[1,62,222],"detection":[2,63,124,223],"in":[3],"low-dose":[4],"computed":[5],"tomography":[6],"(CT)":[7],"images":[8,55],"is":[9,82,190],"essential":[10],"for":[11],"early":[12],"screening":[13],"and":[14,35,56,88,105,112,131,149,153,172,184,208],"treatment":[15],"of":[16,52,60,102,108,122,159,182],"lung":[17,53],"cancer.":[18],"Previous":[19],"related":[20],"researches":[21],"based":[22,97],"on":[23,30,38,98,200],"deep":[24],"convolutional":[25],"neural":[26],"networks":[27],"generally":[28],"rely":[29],"2D":[31],"or":[32],"2.5D":[33],"components":[34],"only":[36],"focus":[37],"the":[39,49,57,106,120,123,133,156,180],"output":[40],"feature":[41,142,161],"information":[42,101,158],"under":[43],"a":[44,67,139,166],"single":[45],"receptive":[46,110],"field.":[47],"Considering":[48],"3D":[50,69,85,99],"nature":[51],"CT":[54,103,203],"performance":[58],"limitation":[59],"state-of-the-art":[61,220],"methods,":[64],"we":[65,137,164],"develop":[66],"novel":[68],"multi-branch":[70],"region":[71],"proposal":[72],"network":[73,125,195],"with":[74,84,128,194],"an":[75],"encoder-decoder":[76],"structure.":[77],"Specifically,":[78],"each":[79],"parallel":[80],"branch":[81],"designed":[83],"residual":[86],"blocks":[87],"U-Net-like":[89],"structure":[90],"to":[91,118,126,145,178],"effectively":[92],"extract":[93],"multi-scale":[94,140],"fusion":[95,143],"features":[96,152],"spatial":[100],"scans,":[104],"strategies":[107],"varying":[109],"fields":[111],"sharing":[113],"weight":[114],"parameters":[115],"are":[116],"used":[117],"improve":[119],"sensitivity":[121],"nodules":[127],"scale":[129],"variation":[130],"maintain":[132],"original":[134],"parameters.":[135],"Besides,":[136],"propose":[138],"attentional":[141],"module":[144],"better":[146],"fuse":[147],"high-resolution":[148],"semantically":[150],"strong":[151],"adaptively":[154],"learn":[155],"inter-dependency":[157],"different":[160],"maps.":[162],"Finally,":[163],"compare":[165],"dynamically":[167],"scaled":[168],"cross":[169],"entropy":[170],"loss":[171],"online":[173],"hard":[174],"example":[175],"mining":[176],"(OHEM)":[177],"combat":[179],"imbalance":[181],"positive":[183],"negative":[185],"samples":[186],"during":[187],"training,":[188],"which":[189],"aimed":[191],"at":[192],"assisting":[193],"optimization.":[196],"Our":[197],"extensive":[198],"experiments":[199],"publicly":[201],"available":[202],"scans":[204],"obtained":[205],"from":[206],"LUNA16":[207],"TianChi":[209],"<sup":[210],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[211],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[212],"competition":[213],"dataset":[214],"demonstrate":[215],"that":[216],"our":[217],"method":[218],"outperform":[219],"pulmonary":[221],"models.":[224]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}