{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T07:10:04Z","timestamp":1779347404118,"version":"3.51.4"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,8]]},"DOI":"10.1109\/isbi61048.2026.11515785","type":"proceedings-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T19:48:46Z","timestamp":1779306526000},"page":"1-5","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection"],"prefix":"10.1109","author":[{"given":"Jihyeon","family":"Baek","sequence":"first","affiliation":[{"name":"VUNO Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seunghoon","family":"Lee","sequence":"additional","affiliation":[{"name":"VUNO Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gitaek","family":"Kwon","sequence":"additional","affiliation":[{"name":"VUNO Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doohyun","family":"Park","sequence":"additional","affiliation":[{"name":"VUNO Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"21464","article-title":"Energy-based out-ofdistribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref2","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume":"31","author":"Lee","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02117-4"},{"key":"ref4","volume-title":"Adam: Automatic detection challenge on age-related macular degeneration","author":"Fu","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01564-3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207664"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106438"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01388-1"},{"key":"ref9","volume-title":"Refuge: Retinal fundus glaucoma challenge","author":"Fu","year":"2019"},{"issue":"2","key":"ref10","doi-asserted-by":"crossref","DOI":"10.3390\/data6020014","article-title":"Retinal fundus multi-disease image dataset (rfmid): A dataset for multi-disease detection research","volume":"6","author":"Pachade","year":"2021","journal-title":"Data"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2024.100929"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-05864-2"},{"key":"ref13","volume-title":"Brain mri dataset","author":"Brima","year":"2021"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-03383-0"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01498-w"},{"key":"ref16","volume-title":"Teeth or dental image dataset","author":"Chaudhary","year":"2024"},{"key":"ref17","volume-title":"Skin diseases and skin cancer recognition dataset","author":"Matin","year":"2023"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"issue":"1","key":"ref19","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41597-020-00622-y","article-title":"HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy","volume":"7","author":"Borgli","year":"2020","journal-title":"Scientific Data"},{"key":"ref20","article-title":"Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions","author":"Engelmann","year":"2022","journal-title":"arXiv preprint"},{"key":"ref21","volume-title":"Indian diabetic retinopathy image dataset (idrid)","author":"Porwal","year":"2018"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3321\/j.issn:0529-6579.2007.z1.029"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"}],"event":{"name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","location":"London, United Kingdom","start":{"date-parts":[[2026,4,8]]},"end":{"date-parts":[[2026,4,11]]}},"container-title":["2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11515282\/11515297\/11515785.pdf?arnumber=11515785","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T06:31:14Z","timestamp":1779345074000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11515785\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/isbi61048.2026.11515785","relation":{},"subject":[],"published":{"date-parts":[[2026,4,8]]}}}