{"id":"https://openalex.org/W4282915608","doi":"https://doi.org/10.1109/tmi.2022.3176915","title":"Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident Learning for Medical Image Segmentation","display_name":"Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident Learning for Medical Image Segmentation","publication_year":2022,"publication_date":"2022-05-23","ids":{"openalex":"https://openalex.org/W4282915608","doi":"https://doi.org/10.1109/tmi.2022.3176915","pmid":"https://pubmed.ncbi.nlm.nih.gov/35604969"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2022.3176915","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3176915","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5090627414","display_name":"Zhe Xu","orcid":"https://orcid.org/0000-0002-1950-0959"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zhe Xu","raw_affiliation_strings":["Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014222179","display_name":"Donghuan Lu","orcid":"https://orcid.org/0000-0002-8399-7410"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]},{"id":"https://openalex.org/I4210103558","display_name":"Tencent Healthcare (China)","ror":"https://ror.org/019xckf23","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donghuan Lu","raw_affiliation_strings":["Tencent Healthcare (Shenzhen) Company Ltd., Shenzhen, China","Tencent Jarvis Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Healthcare (Shenzhen) Company Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210103558"]},{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101735581","display_name":"Jie Luo","orcid":"https://orcid.org/0000-0002-1437-2214"},"institutions":[{"id":"https://openalex.org/I1283280774","display_name":"Brigham and Women's Hospital","ror":"https://ror.org/04b6nzv94","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283280774","https://openalex.org/I48633490"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Luo","raw_affiliation_strings":["Brigham and Women&#x2019;s Hospital, Harvard Medical School, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Brigham and Women&#x2019;s Hospital, Harvard Medical School, Boston, MA, USA","institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100328544","display_name":"Yixin Wang","orcid":"https://orcid.org/0000-0002-8062-0765"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixin Wang","raw_affiliation_strings":["Department of Bioengineering, Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Bioengineering, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013716631","display_name":"Jiangpeng Yan","orcid":"https://orcid.org/0000-0002-0767-1726"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangpeng Yan","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100621577","display_name":"Kai Ma","orcid":"https://orcid.org/0000-0003-2805-3692"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]},{"id":"https://openalex.org/I4210103558","display_name":"Tencent Healthcare (China)","ror":"https://ror.org/019xckf23","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Ma","raw_affiliation_strings":["Tencent Healthcare (Shenzhen) Company Ltd., Shenzhen, China","Tencent Jarvis Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Healthcare (Shenzhen) Company Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210103558"]},{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051649145","display_name":"Yefeng Zheng","orcid":"https://orcid.org/0000-0003-2195-2847"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]},{"id":"https://openalex.org/I4210103558","display_name":"Tencent Healthcare (China)","ror":"https://ror.org/019xckf23","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yefeng Zheng","raw_affiliation_strings":["Tencent Healthcare (Shenzhen) Company Ltd., Shenzhen, China","Tencent Jarvis Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Healthcare (Shenzhen) Company Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210103558"]},{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066840655","display_name":"Raymond Kai\u2010Yu Tong","orcid":"https://orcid.org/0000-0003-4375-653X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Raymond Kai-Yu Tong","raw_affiliation_strings":["Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5090627414"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":6.0111,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.97387718,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"41","issue":"11","first_page":"3062","last_page":"3073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9846000075340271,"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":0.9779000282287598,"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":0.7809910178184509},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7783315181732178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6926230788230896},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.5420982837677002},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.5246173143386841},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47184112668037415},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46093857288360596},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44189783930778503},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.42888009548187256},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38640499114990234},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3369717001914978}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7809910178184509},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7783315181732178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6926230788230896},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.5420982837677002},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.5246173143386841},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47184112668037415},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46093857288360596},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44189783930778503},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.42888009548187256},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38640499114990234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3369717001914978},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tmi.2022.3176915","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3176915","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:35604969","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35604969","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2019646504","https://openalex.org/W2096320880","https://openalex.org/W2098374519","https://openalex.org/W2145305441","https://openalex.org/W2150769593","https://openalex.org/W2396622801","https://openalex.org/W2600383743","https://openalex.org/W2728648333","https://openalex.org/W2809237945","https://openalex.org/W2912934043","https://openalex.org/W2915126261","https://openalex.org/W2918552801","https://openalex.org/W2962914239","https://openalex.org/W2963351448","https://openalex.org/W2963558289","https://openalex.org/W2964227007","https://openalex.org/W2964744899","https://openalex.org/W2966434031","https://openalex.org/W2970546341","https://openalex.org/W2979888373","https://openalex.org/W2979901318","https://openalex.org/W2979907638","https://openalex.org/W3010373058","https://openalex.org/W3011388387","https://openalex.org/W3016113671","https://openalex.org/W3031923721","https://openalex.org/W3091211922","https://openalex.org/W3091593480","https://openalex.org/W3093394156","https://openalex.org/W3094901756","https://openalex.org/W3101310341","https://openalex.org/W3104368407","https://openalex.org/W3156669901","https://openalex.org/W3174890219","https://openalex.org/W3203148062","https://openalex.org/W3203434372","https://openalex.org/W3209458476","https://openalex.org/W4244259635","https://openalex.org/W6698600161","https://openalex.org/W6733814495","https://openalex.org/W6735443497","https://openalex.org/W6743885473","https://openalex.org/W6759274242","https://openalex.org/W6762563763"],"related_works":["https://openalex.org/W4285411112","https://openalex.org/W2085033728","https://openalex.org/W2171299904","https://openalex.org/W2922442631","https://openalex.org/W2053596378","https://openalex.org/W1982025852","https://openalex.org/W2168523118","https://openalex.org/W2106540031","https://openalex.org/W2047970610","https://openalex.org/W3089080437"],"abstract_inverted_index":{"Manually":[0],"segmenting":[1],"medical":[2],"images":[3,138],"is":[4,16,124],"expertise-demanding,":[5],"time-consuming":[6],"and":[7,69,96,113,128,147,171,173],"laborious.":[8],"Acquiring":[9],"massive":[10],"high-quality":[11,22,77,110],"labeled":[12,46,111,117,141],"data":[13,47,59,112],"from":[14,48,105],"experts":[15],"often":[17,31,41],"infeasible.":[18],"Unfortunately,":[19],"without":[20],"sufficient":[21],"pixel-level":[23],"labels,":[24],"the":[25,66,72,132,137,149,182],"usual":[26],"data-driven":[27],"learning-based":[28],"segmentation":[29,104,166,176,184],"methods":[30],"struggle":[32],"with":[33,51,60,167,177],"deficient":[34],"training.":[35],"As":[36],"a":[37,85,93,97,106,121],"result,":[38],"we":[39,83],"are":[40],"forced":[42],"to":[43,101],"collect":[44],"additional":[45,58,133],"multiple":[49],"sources":[50],"varying":[52],"label":[53,98,159,195],"qualities.":[54],"However,":[55],"directly":[56],"introducing":[57],"low-quality":[61,115,140,154],"noisy":[62,116,155,169,179],"labels":[63,156,170,180],"may":[64],"mislead":[65],"network":[67],"training":[68],"undesirably":[70],"offset":[71],"efficacy":[73],"provided":[74],"by":[75,92],"those":[76],"labels.":[78],"To":[79],"address":[80],"this":[81],"issue,":[82],"propose":[84],"Mean-Teacher-assisted":[86],"Confident":[87],"Learning":[88],"(MTCL)":[89],"framework":[90,123],"constructed":[91],"teacher-student":[94],"architecture":[95],"self-denoising":[99],"process":[100],"robustly":[102,129],"learn":[103],"small":[107],"set":[108,142],"of":[109,126,139,152,186],"plentiful":[114],"data.":[118],"Particularly,":[119],"such":[120],"synergistic":[122],"capable":[125],"simultaneously":[127],"exploiting":[130],"(i)":[131],"dark":[134],"knowledge":[135],"inside":[136],"via":[143,157],"perturbation-based":[144],"unsupervised":[145],"consistency,":[146],"(ii)":[148],"productive":[150],"information":[151],"their":[153],"explicit":[158],"refinement.":[160],"Comprehensive":[161],"experiments":[162],"on":[163,194],"left":[164],"atrium":[165],"simulated":[168],"hepatic":[172],"retinal":[174],"vessel":[175],"real-world":[178],"demonstrate":[181],"superior":[183],"performance":[185],"our":[187],"approach":[188],"as":[189,191],"well":[190],"its":[192],"effectiveness":[193],"denoising.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
