{"id":"https://openalex.org/W7161764393","doi":"https://doi.org/10.1109/isbi61048.2026.11515785","title":"A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection","display_name":"A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161764393","doi":"https://doi.org/10.1109/isbi61048.2026.11515785"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515785","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515785","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":"conference-paper","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/A5136521897","display_name":"Jihyeon Baek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jihyeon Baek","raw_affiliation_strings":["VUNO Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VUNO Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136513808","display_name":"Seunghoon Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seunghoon Lee","raw_affiliation_strings":["VUNO Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VUNO Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032781660","display_name":"Gitaek Kwon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gitaek Kwon","raw_affiliation_strings":["VUNO Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VUNO Inc","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136553629","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":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6080571,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.11909999698400497,"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":0.11909999698400497,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.08009999990463257,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07559999823570251,"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/deep-learning","display_name":"Deep learning","score":0.7310000061988831},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3280999958515167},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3012999892234802},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3009999990463257},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.29190000891685486}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7724999785423279},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7310000061988831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6200000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5047000050544739},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515785","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515785","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2008359794","https://openalex.org/W2056132907","https://openalex.org/W2765793020","https://openalex.org/W3082604781","https://openalex.org/W3091479204","https://openalex.org/W3114128166","https://openalex.org/W3128220181","https://openalex.org/W4282048668","https://openalex.org/W4289844305","https://openalex.org/W4311968348","https://openalex.org/W4391643750","https://openalex.org/W4399641997","https://openalex.org/W4399929809","https://openalex.org/W4413846782"],"related_works":[],"abstract_inverted_index":{"Out-of-distribution":[0],"(OOD)":[1],"detection":[2,51,105,130],"is":[3,25],"essential":[4],"for":[5,15,128],"building":[6],"reliable":[7],"AI":[8],"systems,":[9],"as":[10],"models":[11],"that":[12,127],"produce":[13],"outputs":[14],"invalid":[16],"inputs":[17],"cannot":[18],"be":[19],"trusted.":[20],"Although":[21],"deep":[22],"learning":[23,32],"(DL)":[24],"often":[26],"assumed":[27],"to":[28,44],"outperform":[29],"traditional":[30],"machine":[31],"(ML),":[33],"medical":[34],"imaging":[35],"data":[36],"are":[37,69],"typically":[38],"acquired":[39],"under":[40],"standardized":[41],"protocols,":[42],"leading":[43],"relatively":[45],"constrained":[46],"image":[47],"variability":[48],"in":[49,63],"OOD":[50,129],"tasks.":[52],"This":[53],"motivates":[54],"a":[55],"direct":[56],"comparison":[57],"between":[58,93],"ML":[59,108,137],"and":[60,78,91,95,99],"DL":[61],"approaches":[62,68,85,138],"this":[64],"setting.":[65],"The":[66,107],"two":[67],"evaluated":[70],"on":[71,97],"open":[72],"datasets":[73],"comprising":[74],"over":[75],"60,000":[76],"fundus":[77],"non-fundus":[79],"images":[80],"across":[81],"multiple":[82],"resolutions.":[83],"Both":[84],"achieved":[86],"an":[87],"AUROC":[88],"of":[89,132],"1.000":[90,96],"accuracies":[92],"0.999":[94],"internal":[98],"external":[100],"validation":[101],"sets,":[102],"showing":[103],"comparable":[104],"performance.":[106],"approach,":[109],"however,":[110],"exhibited":[111],"substantially":[112],"lower":[113],"end-to-end":[114],"latency":[115],"while":[116],"maintaining":[117],"equivalent":[118],"accuracy,":[119],"indicating":[120],"greater":[121],"computational":[122,146],"efficiency.":[123],"These":[124],"results":[125],"suggest":[126],"tasks":[131],"limited":[133],"visual":[134],"complexity,":[135],"lightweight":[136],"can":[139],"achieve":[140],"DL-level":[141],"performance":[142],"with":[143],"significantly":[144],"reduced":[145],"cost,":[147],"supporting":[148],"practical":[149],"real-world":[150],"deployment.":[151]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-21T00:00:00"}
