{"id":"https://openalex.org/W4401749940","doi":"https://doi.org/10.1109/isbi56570.2024.10635511","title":"Monte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning","display_name":"Monte-Carlo Frequency Dropout for Predictive Uncertainty Estimation in Deep Learning","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401749940","doi":"https://doi.org/10.1109/isbi56570.2024.10635511"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 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/A5064608267","display_name":"Tal Zeevi","orcid":"https://orcid.org/0000-0001-8917-7061"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tal Zeevi","raw_affiliation_strings":["Yale University,Departments of Biomedical Engineering,New Haven,CT,USA"],"affiliations":[{"raw_affiliation_string":"Yale University,Departments of Biomedical Engineering,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102921911","display_name":"Rajesh Venkataraman","orcid":"https://orcid.org/0009-0001-2554-4130"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajesh Venkataraman","raw_affiliation_strings":["Eigen Health,Grass Valley,CA,USA"],"affiliations":[{"raw_affiliation_string":"Eigen Health,Grass Valley,CA,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078494006","display_name":"Lawrence H. Staib","orcid":"https://orcid.org/0000-0002-9516-5136"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence H. Staib","raw_affiliation_strings":["Yale University,Departments of Biomedical Engineering,New Haven,CT,USA"],"affiliations":[{"raw_affiliation_string":"Yale University,Departments of Biomedical Engineering,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083553196","display_name":"John A. Onofrey","orcid":"https://orcid.org/0000-0002-9432-0448"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John A. Onofrey","raw_affiliation_strings":["Yale University,Departments of Biomedical Engineering,New Haven,CT,USA"],"affiliations":[{"raw_affiliation_string":"Yale University,Departments of Biomedical Engineering,New Haven,CT,USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5064608267"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":2.9096,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91993544,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9932000041007996,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9932000041007996,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9876000285148621,"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/T12560","display_name":"Nuclear Engineering Thermal-Hydraulics","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.8244747519493103},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.7559509873390198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5536389350891113},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.421367883682251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3797905147075653},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3304100036621094},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2307228446006775},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13757944107055664},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1256117820739746}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.8244747519493103},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7559509873390198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5536389350891113},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.421367883682251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3797905147075653},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3304100036621094},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2307228446006775},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13757944107055664},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1256117820739746},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338440","display_name":"HORIZON EUROPE Health","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1987555621","https://openalex.org/W2095705004","https://openalex.org/W2101946573","https://openalex.org/W2903179019","https://openalex.org/W2950896278","https://openalex.org/W2963190567","https://openalex.org/W2964059111","https://openalex.org/W2964331958","https://openalex.org/W2964478178","https://openalex.org/W3095948197","https://openalex.org/W3111980464","https://openalex.org/W3135126155","https://openalex.org/W3164330620","https://openalex.org/W3177320218","https://openalex.org/W4321232182","https://openalex.org/W4362453841","https://openalex.org/W6600213771","https://openalex.org/W6674330103","https://openalex.org/W6675108834","https://openalex.org/W6737164272","https://openalex.org/W6754772488","https://openalex.org/W6756483922","https://openalex.org/W6766254915","https://openalex.org/W6786212429"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Monte-Carlo":[0],"(MC)":[1],"dropout":[2,59,100,108,134],"is":[3,140],"a":[4,29,61,81,94,119,129],"technique":[5],"for":[6,142],"estimating":[7],"predictive":[8],"uncertainty":[9],"in":[10,48,80,135],"artificial":[11],"neural":[12],"networks":[13],"(ANNs)":[14],"by":[15],"leveraging":[16],"an":[17,24],"ensemble":[18],"of":[19,23,57,65,104,110,122,132,145],"randomly":[20],"diluted":[21],"instances":[22],"optimized":[25],"ANN":[26,73,105,111],"to":[27],"construct":[28],"posterior":[30],"prediction":[31,83],"distribution.":[32],"However,":[33],"classical":[34],"MC-dropout,":[35],"operating":[36],"within":[37],"the":[38,55,72,114,143,146],"neuronal":[39],"signal":[40,66],"space,":[41],"might":[42],"overlook":[43],"frequency-related":[44],"uncertainties":[45],"commonly":[46],"observed":[47],"medical":[49],"imaging":[50,124],"data.":[51],"This":[52],"study":[53],"explores":[54],"concept":[56],"MC-frequency":[58,133],"\u2013":[60],"repetitive":[62],"stochastic":[63],"attenuation":[64],"frequencies":[67],"as":[68],"they":[69],"flow":[70],"through":[71],"during":[74],"inference,":[75],"and":[76,107],"assessing":[77],"its":[78],"efficacy":[79],"selective":[82],"scenario,":[84],"wherein":[85],"decision-makers":[86],"can":[87],"reject":[88],"uncertain":[89],"model":[90],"predictions.":[91],"We":[92],"conducted":[93],"comprehensive":[95],"comparison":[96],"against":[97],"state-of-the-art":[98],"MC-signal":[99],"approaches:":[101],"drop-connect":[102],"(dilution":[103,109],"edges)":[106],"nodes)":[112],"on":[113],"public":[115],"MedMNIST":[116],"repository.":[117],"Covering":[118],"diverse":[120],"set":[121],"biomedical":[123],"data,":[125],"our":[126],"experiments":[127],"demonstrated":[128],"statistical":[130],"advantage":[131],"most":[136],"benchmarks.":[137],"Our":[138],"source-code":[139],"accessible":[141],"benefit":[144],"community.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":8}],"updated_date":"2026-02-25T08:12:03.925757","created_date":"2025-10-10T00:00:00"}