{"id":"https://openalex.org/W4417250522","doi":"https://doi.org/10.1109/bibe66822.2025.00060","title":"Single-View Cotraining and Active Learning for Concurrent Medical Activity Labeling","display_name":"Single-View Cotraining and Active Learning for Concurrent Medical Activity Labeling","publication_year":2025,"publication_date":"2025-11-06","ids":{"openalex":"https://openalex.org/W4417250522","doi":"https://doi.org/10.1109/bibe66822.2025.00060"},"language":null,"primary_location":{"id":"doi:10.1109/bibe66822.2025.00060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe66822.2025.00060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE)","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/A5022064950","display_name":"Aydin Saribudak","orcid":"https://orcid.org/0000-0003-0792-1251"},"institutions":[{"id":"https://openalex.org/I4210109165","display_name":"Environmental and Occupational Health Sciences Institute","ror":"https://ror.org/01vta4r13","country_code":"US","type":"education","lineage":["https://openalex.org/I4210109165"]},{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aydin Saribudak","raw_affiliation_strings":["Rutgers University,Electrical and Computer Engineering,Piscataway,NJ,USA"],"affiliations":[{"raw_affiliation_string":"Rutgers University,Electrical and Computer Engineering,Piscataway,NJ,USA","institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4210109165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104878141","display_name":"Aaron H. Mun","orcid":"https://orcid.org/0009-0001-8579-8052"},"institutions":[{"id":"https://openalex.org/I1336742384","display_name":"Children's National","ror":"https://ror.org/03wa2q724","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1336742384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aaron H. Mun","raw_affiliation_strings":["Children&#x0027;s National Hospital,Division of Trauma and Burn Surgery,Washington,DC,USA"],"affiliations":[{"raw_affiliation_string":"Children&#x0027;s National Hospital,Division of Trauma and Burn Surgery,Washington,DC,USA","institution_ids":["https://openalex.org/I1336742384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068126329","display_name":"Ivan Marsic","orcid":"https://orcid.org/0000-0002-1033-6865"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]},{"id":"https://openalex.org/I4210109165","display_name":"Environmental and Occupational Health Sciences Institute","ror":"https://ror.org/01vta4r13","country_code":"US","type":"education","lineage":["https://openalex.org/I4210109165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ivan Marsic","raw_affiliation_strings":["Rutgers University,Electrical and Computer Engineering,Piscataway,NJ,USA"],"affiliations":[{"raw_affiliation_string":"Rutgers University,Electrical and Computer Engineering,Piscataway,NJ,USA","institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4210109165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5022064950"],"corresponding_institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4210109165"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5461234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"317","last_page":"325"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.21649999916553497,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency 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/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.21649999916553497,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency 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/T10812","display_name":"Human Pose and Action Recognition","score":0.09610000252723694,"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/T11238","display_name":"Simulation-Based Education in Healthcare","score":0.08049999922513962,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/active-learning","display_name":"Active learning (machine learning)","score":0.7470999956130981},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5425000190734863},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4668000042438507},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.4587000012397766},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.42309999465942383},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41679999232292175},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37940001487731934}],"concepts":[{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.7470999956130981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6172000169754028},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5895000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5638999938964844},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5425000190734863},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4587000012397766},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41679999232292175},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3098999857902527},{"id":"https://openalex.org/C3019041143","wikidata":"https://www.wikidata.org/wiki/Q12204","display_name":"Active tuberculosis","level":4,"score":0.30550000071525574},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.26100000739097595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibe66822.2025.00060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe66822.2025.00060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6118814437","display_name":null,"funder_award_id":"R01LM011834","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1824737917","https://openalex.org/W1909740415","https://openalex.org/W2026498605","https://openalex.org/W2028344184","https://openalex.org/W2041453872","https://openalex.org/W2044822643","https://openalex.org/W2048679005","https://openalex.org/W2104660959","https://openalex.org/W2108307068","https://openalex.org/W2117614111","https://openalex.org/W2133556223","https://openalex.org/W2140076625","https://openalex.org/W2288807527","https://openalex.org/W2296039095","https://openalex.org/W2341283081","https://openalex.org/W2620069838","https://openalex.org/W2963326042","https://openalex.org/W2963524571","https://openalex.org/W2977942577","https://openalex.org/W3000482929","https://openalex.org/W3214423317","https://openalex.org/W4301229630","https://openalex.org/W4405662792"],"related_works":[],"abstract_inverted_index":{"Labeling":[0],"medical":[1],"activities":[2,87],"from":[3,37],"visual":[4],"datasets":[5,24],"requires":[6],"extensive":[7],"time":[8,134,146,174,210],"and":[9,81,104,193,206],"effort":[10],"for":[11,25,112,122,136,147,162,168,176,185,191,197,217],"practitioners.":[12],"Deep":[13],"learning":[14,32,47,63],"methods":[15,221],"have":[16],"been":[17],"widely":[18],"used":[19,42],"to":[20,48,56,84,98,155,222],"train":[21],"partially":[22],"labeled":[23],"predicting":[26],"activity":[27,228],"labels.":[28],"Cotraining,":[29],"a":[30,54,156],"semi-supervised":[31],"technique":[33],"that":[34],"utilizes":[35],"predictions":[36,178],"two":[38],"different":[39],"models,":[40],"is":[41,53],"in":[43,88,203,208],"conjunction":[44],"with":[45,61,213,226],"active":[46,62,82],"mitigate":[49],"sampling":[50],"bias.":[51],"There":[52],"need":[55],"optimize":[57],"manual":[58,144],"labeling":[59,133,173,209],"efforts":[60],"while":[64],"improving":[65],"the":[66,74,94,102,114,118,128,131,143,172,198],"prediction":[67,110,204],"performance.":[68],"In":[69],"this":[70],"paper,":[71],"we":[72,211],"developed":[73],"SCALE-TAG":[75],"methodology,":[76],"based":[77,108],"on":[78,109],"single-view":[79],"cotraining":[80],"learning,":[83],"label":[85],"concurrent":[86],"video":[89],"records":[90],"cost-effectively.":[91],"ScALE-TAG":[92],"utilized":[93],"cosine":[95],"similarity":[96],"metric":[97],"enhance":[99],"diversity":[100],"within":[101],"subsets":[103],"selected":[105,137],"informative":[106],"samples":[107,138,164,181],"uncertainty":[111],"retraining":[113],"models.":[115],"We":[116,151],"evaluated":[117],"performance":[119],"of":[120,142,179,187],"scale-tag":[121,154],"six":[123],"trauma":[124],"resuscitation":[125],"activities.":[126],"During":[127],"training":[129],"stage,":[130],"average":[132],"required":[135,175],"was":[139,182],"10.93":[140],"%":[141,184,190,196],"annotation":[145],"all":[148],"unlabeled":[149],"samples.":[150],"then":[152],"applied":[153],"test":[157,163,180],"set.":[158],"The":[159,201],"F1":[160],"scores":[161],"were":[165],"over":[166],"0.7":[167],"four":[169],"activities,":[170],"where":[171],"ambiguous":[177],"8":[183],"one":[186],"them,":[188],"17":[189],"another,":[192],"around":[194],"20":[195],"other":[199],"two.":[200],"improvement":[202],"accuracy":[205],"decrease":[207],"obtained":[212],"ScAleTAG":[214],"are":[215],"promising":[216],"developing":[218],"deep":[219],"learning-based":[220],"support":[223],"healthcare":[224],"providers":[225],"effective":[227],"labeling.":[229]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-11T00:00:00"}
