{"id":"https://openalex.org/W1967321700","doi":"https://doi.org/10.1109/cvpr.2012.6247770","title":"The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle segmentation in ultrasound","display_name":"The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle segmentation in ultrasound","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W1967321700","doi":"https://doi.org/10.1109/cvpr.2012.6247770","mag":"1967321700"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6247770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","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/A5029215323","display_name":"Gustavo Carneiro","orcid":"https://orcid.org/0000-0002-5571-6220"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"G. Carneiro","raw_affiliation_strings":["Australian Centre for Visual Technologies, University of Adelaide, Australia","[Australian Centre for Visual Technologies, The University of Adelaide, Australia]"],"affiliations":[{"raw_affiliation_string":"Australian Centre for Visual Technologies, University of Adelaide, Australia","institution_ids":["https://openalex.org/I5681781"]},{"raw_affiliation_string":"[Australian Centre for Visual Technologies, The University of Adelaide, Australia]","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078047670","display_name":"Jacinto C. Nascimento","orcid":"https://orcid.org/0000-0001-7468-5127"},"institutions":[{"id":"https://openalex.org/I4387152517","display_name":"Instituto Superior T\u00e9cnico","ror":"https://ror.org/03db2by73","country_code":null,"type":"education","lineage":["https://openalex.org/I141596103","https://openalex.org/I4387152517"]},{"id":"https://openalex.org/I4210109601","display_name":"Instituto de Engenharia de Sistemas e Computadores Microsistemas e Nanotecnologias","ror":"https://ror.org/022mzwp71","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210109601","https://openalex.org/I4210125590"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"J. C. Nascimento","raw_affiliation_strings":["Instituto de Sistemas e Rob\u00f3tica, Instituto Superior T\u00e9cnico, Portugal","Instituto de Sistemas e Robotica, Instituto Superior Te\u00b4cnico, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto de Sistemas e Rob\u00f3tica, Instituto Superior T\u00e9cnico, Portugal","institution_ids":["https://openalex.org/I4210109601","https://openalex.org/I4387152517"]},{"raw_affiliation_string":"Instituto de Sistemas e Robotica, Instituto Superior Te\u00b4cnico, Portugal","institution_ids":["https://openalex.org/I4210109601","https://openalex.org/I4387152517"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5029215323"],"corresponding_institution_ids":["https://openalex.org/I5681781"],"apc_list":null,"apc_paid":null,"fwci":0.7577,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74586874,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"948","last_page":"955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular 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/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular 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/T10892","display_name":"Drilling and Well Engineering","score":0.9538000226020813,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9521999955177307,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7660952806472778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7292748689651489},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7268840074539185},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.589777946472168},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5847156047821045},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.47831180691719055},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.477912038564682},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.46717318892478943},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4639202356338501},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45772653818130493},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4317367374897003},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4125310778617859},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38492119312286377}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7660952806472778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7292748689651489},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7268840074539185},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.589777946472168},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5847156047821045},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.47831180691719055},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.477912038564682},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.46717318892478943},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4639202356338501},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45772653818130493},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4317367374897003},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4125310778617859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38492119312286377},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2012.6247770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:digital.library.adelaide.edu.au:2440/73870","is_oa":false,"landing_page_url":"http://hdl.handle.net/2440/73870","pdf_url":null,"source":{"id":"https://openalex.org/S4306401835","display_name":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I5681781","host_organization_name":"The University of Adelaide","host_organization_lineage":["https://openalex.org/I5681781"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://dx.doi.org/10.1109/cvpr.2012.6247770","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1489207701","https://openalex.org/W2028582824","https://openalex.org/W2048679005","https://openalex.org/W2079057609","https://openalex.org/W2081884954","https://openalex.org/W2100495367","https://openalex.org/W2101689475","https://openalex.org/W2110297148","https://openalex.org/W2112691218","https://openalex.org/W2120219904","https://openalex.org/W2121680631","https://openalex.org/W2128614648","https://openalex.org/W2136504847","https://openalex.org/W2146866435","https://openalex.org/W2153927146","https://openalex.org/W2154582710","https://openalex.org/W2155704307","https://openalex.org/W2160754664","https://openalex.org/W2164202775","https://openalex.org/W2167825066","https://openalex.org/W2251236599","https://openalex.org/W2571050459","https://openalex.org/W3187867541","https://openalex.org/W6679131053","https://openalex.org/W6680140577"],"related_works":["https://openalex.org/W2494523064","https://openalex.org/W2943623134","https://openalex.org/W2588219639","https://openalex.org/W2030292806","https://openalex.org/W2002739602","https://openalex.org/W2345647014","https://openalex.org/W2201192772","https://openalex.org/W3136891595","https://openalex.org/W2535030201","https://openalex.org/W1964819397"],"abstract_inverted_index":{"The":[0,27],"use":[1,189],"of":[2,12,34,52,84,99,110,120,131,137,150,178,190,195],"statistical":[3,54],"pattern":[4],"recognition":[5],"models":[6,87],"to":[7,123,164,174,216],"segment":[8],"the":[9,13,23,31,39,50,53,68,81,103,108,111,118,135,143,146,165,170,175,183,188,191,198],"left":[10,220],"ventricle":[11,221],"heart":[14],"in":[15,38,197],"ultrasound":[16],"images":[17],"has":[18,134,172],"gained":[19],"substantial":[20],"attention":[21],"over":[22],"last":[24],"few":[25],"years.":[26],"main":[28],"obstacle":[29],"for":[30,41,49,70,74],"wider":[32],"exploration":[33],"this":[35,58],"methodology":[36,104],"lies":[37],"need":[40,69],"large":[42,71],"annotated":[43,92,239],"training":[44,72,93,180,233],"sets,":[45],"which":[46],"are":[47,154],"used":[48],"estimation":[51],"model":[55,206],"parameters.":[56],"In":[57],"paper,":[59],"we":[60,186],"present":[61],"a":[62,89,100,151,159,217,229],"new":[63,193],"on-line":[64,129],"co-training":[65,199],"methodologythat":[66],"reduces":[67],"sets":[73,234],"such":[75],"parameter":[76],"estimation.":[77],"Our":[78],"approach":[79,133],"learns":[80],"initial":[82],"parameters":[83],"two":[85],"different":[86],"using":[88],"small":[90],"manually":[91],"set.":[94],"Then,":[95],"given":[96],"each":[97,125],"frame":[98],"test":[101,152],"sequence,":[102],"not":[105],"only":[106],"produces":[107],"segmentation":[109,139,222],"current":[112],"frame,":[113],"but":[114,156],"it":[115,157],"also":[116],"uses":[117],"results":[119,140],"both":[121],"classifiers":[122,144,196],"retrain":[124],"other":[126],"incrementally.":[127],"This":[128],"aspect":[130],"our":[132,213],"advantages":[136],"producing":[138],"and":[141,204],"retraining":[142],"on":[145,228],"fly":[147],"as":[148],"frames":[149],"sequence":[153],"presented,":[155],"introduces":[158],"harder":[160],"learning":[161],"setting":[162],"compared":[163],"usual":[166],"off-line":[167],"co-training,":[168],"where":[169],"algorithm":[171],"access":[173],"whole":[176],"set":[177],"un-annotated":[179],"samples":[181],"from":[182],"beginning.":[184],"Moreover,":[185],"introduce":[187],"following":[192],"types":[194],"framework:":[200],"deep":[201],"belief":[202],"network":[203],"multiple":[205],"probabilistic":[207],"data":[208],"association.":[209],"We":[210],"show":[211],"that":[212,224],"method":[214],"leads":[215],"fully":[218],"automatic":[219],"system":[223],"achieves":[225],"state-of-the-art":[226],"accuracy":[227],"public":[230],"database":[231],"with":[232],"containing":[235],"at":[236],"least":[237],"twenty":[238],"images.":[240]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
