{"id":"https://openalex.org/W7161838182","doi":"https://doi.org/10.1109/isbi61048.2026.11515880","title":"Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation","display_name":"Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161838182","doi":"https://doi.org/10.1109/isbi61048.2026.11515880"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","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/A5135920556","display_name":"Inhyuk Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Inhyuk Park","raw_affiliation_strings":["VUNO Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VUNO Inc","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135942384","display_name":"Doohyun Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doohyun Park","raw_affiliation_strings":["VUNO Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VUNO Inc","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5135920556"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94148164,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.42890000343322754,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.42890000343322754,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.02930000051856041,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.029100000858306885,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4814000129699707},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3068000078201294},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.2770000100135803},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.257099986076355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5659999847412109},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5471000075340271},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44519999623298645},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31049999594688416},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26980000734329224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26260000467300415},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.57243412733078,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1534477342","https://openalex.org/W2963697299","https://openalex.org/W2964292098","https://openalex.org/W2981873476","https://openalex.org/W2988966271","https://openalex.org/W3042609801","https://openalex.org/W3156669901","https://openalex.org/W4287947470","https://openalex.org/W4390872651","https://openalex.org/W4394779364","https://openalex.org/W4399153297","https://openalex.org/W4400126525","https://openalex.org/W4403015917","https://openalex.org/W4403069814","https://openalex.org/W4413051347"],"related_works":[],"abstract_inverted_index":{"Noisy":[0],"labels":[1,31],"are":[2,108],"common":[3],"in":[4],"large-scale":[5],"medical":[6],"imaging":[7],"datasets":[8],"due":[9],"to":[10],"inter-observer":[11],"variability":[12],"and":[13,21,65,72,95,124],"ambiguous":[14,117],"cases.":[15],"We":[16],"propose":[17],"a":[18,57,79],"statistically":[19,73],"grounded":[20],"task-agnostic":[22],"framework,":[23],"Standardized":[24],"Loss":[25],"Aggregation":[26],"(SLA),":[27],"for":[28,128],"detecting":[29],"noisy":[30],"at":[32],"the":[33,63,88],"sample":[34],"level.":[35],"SLA":[36,85,113],"quantifies":[37],"label":[38],"reliability":[39,127],"by":[40],"aggregating":[41],"standardized":[42],"fold-level":[43],"validation":[44],"losses":[45],"across":[46,91],"repeated":[47],"cross-validation":[48],"runs.":[49],"This":[50],"formulation":[51],"generalizes":[52],"discrete":[53],"hard-counting":[54,89],"schemes":[55],"into":[56],"continuous":[58],"estimator":[59],"that":[60,84],"captures":[61],"both":[62],"frequency":[64],"magnitude":[66],"of":[67],"performance":[68],"deviations,":[69],"yielding":[70],"interpretable":[71],"stable":[74],"noisiness":[75],"scores.":[76],"Experiments":[77],"on":[78],"public":[80],"fundus":[81],"dataset":[82,126],"demonstrate":[83],"consistently":[86],"outperforms":[87],"baseline":[90],"all":[92],"noise":[93,102],"levels":[94],"converges":[96],"substantially":[97],"faster,":[98],"especially":[99],"under":[100],"low":[101],"ratios":[103],"where":[104],"subtle":[105],"loss":[106],"variations":[107],"informative.":[109],"Samples":[110],"with":[111],"high":[112],"scores":[114],"indicate":[115],"potentially":[116],"or":[118],"mislabeled":[119],"cases,":[120],"guiding":[121],"efficient":[122],"re-annotation":[123],"improving":[125],"any":[129],"classification":[130],"task.":[131]},"counts_by_year":[],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2026-05-21T00:00:00"}