{"id":"https://openalex.org/W4391306342","doi":"https://doi.org/10.1109/smc53992.2023.10393883","title":"Semi-supervised Network for Thyroid Nodule Segmentation via Joint Consistency Learning and Co-Training","display_name":"Semi-supervised Network for Thyroid Nodule Segmentation via Joint Consistency Learning and Co-Training","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4391306342","doi":"https://doi.org/10.1109/smc53992.2023.10393883"},"language":"en","primary_location":{"id":"doi:10.1109/smc53992.2023.10393883","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/smc53992.2023.10393883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5101762395","display_name":"Li Dai","orcid":"https://orcid.org/0009-0003-9444-4357"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Dai","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China","School of Information Science and Engineering, Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]},{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028538102","display_name":"Guijuan Zhang","orcid":"https://orcid.org/0000-0002-9545-8668"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guijuan Zhang","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China","School of Information Science and Engineering, Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]},{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lei Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Lyu","raw_affiliation_strings":["School of Information Science and Engineering, Shandong Normal University,Jinan,China","School of Information Science and Engineering, Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]},{"raw_affiliation_string":"School of Information Science and Engineering, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28006308"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"474","last_page":"479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9769999980926514,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9769999980926514,"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/computer-science","display_name":"Computer science","score":0.6826009750366211},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6579340696334839},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6218010783195496},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5927517414093018},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.5809741020202637},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5637258291244507},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5372661352157593},{"id":"https://openalex.org/keywords/thyroid","display_name":"Thyroid","score":0.5060345530509949},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3963109254837036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39092808961868286},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.19264063239097595},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11068761348724365},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10001036524772644},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09571701288223267},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06924712657928467}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6826009750366211},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6579340696334839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6218010783195496},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5927517414093018},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.5809741020202637},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5637258291244507},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5372661352157593},{"id":"https://openalex.org/C526584372","wikidata":"https://www.wikidata.org/wiki/Q16399","display_name":"Thyroid","level":2,"score":0.5060345530509949},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3963109254837036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39092808961868286},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.19264063239097595},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11068761348724365},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10001036524772644},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09571701288223267},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06924712657928467},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53992.2023.10393883","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/smc53992.2023.10393883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[{"id":"https://openalex.org/G6038277628","display_name":null,"funder_award_id":"61976127","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W360623563","https://openalex.org/W1901129140","https://openalex.org/W2560023338","https://openalex.org/W2741754476","https://openalex.org/W2884436604","https://openalex.org/W2900728345","https://openalex.org/W2910307295","https://openalex.org/W2911470197","https://openalex.org/W2953070460","https://openalex.org/W2963073217","https://openalex.org/W2964309882","https://openalex.org/W2978426779","https://openalex.org/W2979433110","https://openalex.org/W2984043001","https://openalex.org/W3002393520","https://openalex.org/W3014974815","https://openalex.org/W3035680157","https://openalex.org/W3084196418","https://openalex.org/W3109470666","https://openalex.org/W3112701542","https://openalex.org/W3127751679","https://openalex.org/W3171581326","https://openalex.org/W3204269530","https://openalex.org/W3211021361","https://openalex.org/W4205307011","https://openalex.org/W4213423227","https://openalex.org/W4221140066","https://openalex.org/W4224920134","https://openalex.org/W4226503691","https://openalex.org/W4280631251","https://openalex.org/W4321232185","https://openalex.org/W6612134913","https://openalex.org/W6733814495","https://openalex.org/W6762913911","https://openalex.org/W6810624242","https://openalex.org/W6810755270","https://openalex.org/W6811401537"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W1964806738","https://openalex.org/W4243779904","https://openalex.org/W4377691549","https://openalex.org/W2056973590","https://openalex.org/W2748574964","https://openalex.org/W1996130883","https://openalex.org/W1603736412","https://openalex.org/W2331635898","https://openalex.org/W2107939670"],"abstract_inverted_index":{"Thyroid":[0],"nodule":[1,32,88],"is":[2],"a":[3,47,69,85,135,194],"common":[4],"clinical":[5],"disease,":[6],"although":[7],"most":[8],"nodules":[9],"are":[10,40],"benign,":[11],"the":[12,56,107,115,119,128,141,147,156,170,174,191,201,207],"incidence":[13],"rate":[14],"of":[15,50,72,155,197],"thyroid":[16,31,87],"cancer":[17],"has":[18],"risen":[19],"rapidly":[20],"in":[21,68,76],"recent":[22],"years.":[23],"Even":[24],"though":[25],"many":[26],"methods":[27,39],"have":[28],"achieved":[29],"automated":[30],"segmentation":[33,89],"based":[34,41,111],"on":[35,42,112,203],"deep":[36],"learning,":[37],"these":[38],"supervised":[43],"learning":[44,94,103,109],"and":[45,74,95,104,150,159,173,188],"require":[46],"large":[48],"amount":[49,196],"labeled":[51,198,204],"data":[52,205],"for":[53],"training.":[54],"However,":[55],"labeling":[57],"work":[58],"must":[59],"be":[60],"carried":[61],"out":[62],"by":[63],"professional":[64],"doctors,":[65],"which":[66],"results":[67],"small":[70,195],"number":[71],"datasets":[73],"difficulty":[75],"labeling.":[77],"To":[78],"address":[79],"this":[80,82],"problem,":[81],"paper":[83],"proposes":[84],"semi-supervised":[86,178],"model":[90,98,117,121,130,149,172,179,192,208],"via":[91],"joint":[92],"consistency":[93,102,108],"co-training.":[96,105],"This":[97,177],"includes":[99],"two":[100],"branches:":[101],"In":[106,124,144],"branch,":[110,146],"consistent":[113,165],"regularization,":[114],"teacher":[116,129,142,148,171],"guides":[118],"student":[120,175],"to":[122,126,138,163],"optimize.":[123],"order":[125],"make":[127],"more":[131],"stable,":[132],"we":[133],"design":[134],"co-training":[136,145],"framework":[137],"further":[139],"optimize":[140],"model.":[143,176],"TransUNet":[151],"extract":[152],"different":[153],"representations":[154,184],"same":[157],"sample":[158],"teach":[160],"each":[161],"other":[162],"prevent":[164],"but":[166],"incorrect":[167],"predictions":[168],"between":[169],"can":[180],"learns":[181],"useful":[182],"feature":[183],"from":[185],"unlabeled":[186],"data,":[187,199],"effectively":[189],"trains":[190],"with":[193],"reducing":[200],"dependence":[202],"during":[206],"training":[209],"process.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}