{"id":"https://openalex.org/W3201662874","doi":"https://doi.org/10.48550/arxiv.2211.12717","title":"Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks","display_name":"Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks","publication_year":2022,"publication_date":"2022-11-23","ids":{"openalex":"https://openalex.org/W3201662874","doi":"https://doi.org/10.48550/arxiv.2211.12717","mag":"3201662874"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2211.12717","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.12717","pdf_url":"https://arxiv.org/pdf/2211.12717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2211.12717","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030721728","display_name":"Neil Band","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Band, Neil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031809940","display_name":"Tim G. J. Rudner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rudner, Tim G. J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102428192","display_name":"Qixuan Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Qixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054835148","display_name":"Angelos Filos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Filos, Angelos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016611202","display_name":"Zachary Nado","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nado, Zachary","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050114946","display_name":"Michael W. Dusenberry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dusenberry, Michael W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048204767","display_name":"Ghassen Jerfel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jerfel, Ghassen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081819708","display_name":"Dustin Tran","orcid":"https://orcid.org/0000-0002-3715-5378"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Dustin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029186201","display_name":"Yarin Gal","orcid":"https://orcid.org/0000-0002-2733-2078"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gal, Yarin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965000152587891,"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":0.9965000152587891,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9940000176429749,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/benchmarking","display_name":"Benchmarking","score":0.8081440925598145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7799373865127563},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7787075042724609},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7690309286117554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7156169414520264},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6977080702781677},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6724594235420227},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.45525890588760376},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4136476218700409}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8081440925598145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7799373865127563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7787075042724609},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7690309286117554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7156169414520264},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6977080702781677},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6724594235420227},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.45525890588760376},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4136476218700409},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2211.12717","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.12717","pdf_url":"https://arxiv.org/pdf/2211.12717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2211.12717","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2211.12717","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2211.12717","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.12717","pdf_url":"https://arxiv.org/pdf/2211.12717","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309949","display_name":"Canadian Institute for Advanced Research","ror":"https://ror.org/01sdtdd95"},{"id":"https://openalex.org/F4320320290","display_name":"University of Oxford","ror":"https://ror.org/052gg0110"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2047229728","https://openalex.org/W2088336077","https://openalex.org/W2099111195","https://openalex.org/W2103328396","https://openalex.org/W2108677974","https://openalex.org/W2111051539","https://openalex.org/W2120340025","https://openalex.org/W2150066425","https://openalex.org/W2166851633","https://openalex.org/W2194775991","https://openalex.org/W2301358467","https://openalex.org/W2480078828","https://openalex.org/W2523058522","https://openalex.org/W2600383743","https://openalex.org/W2725061391","https://openalex.org/W2751448052","https://openalex.org/W2753558426","https://openalex.org/W2788755844","https://openalex.org/W2792388013","https://openalex.org/W2803487511","https://openalex.org/W2951965145","https://openalex.org/W2963024893","https://openalex.org/W2963215553","https://openalex.org/W2963238274","https://openalex.org/W2964059111","https://openalex.org/W2970861023","https://openalex.org/W2970961383","https://openalex.org/W2971561076","https://openalex.org/W2993383518","https://openalex.org/W3011680720","https://openalex.org/W3037583715","https://openalex.org/W3109915642","https://openalex.org/W3112486745","https://openalex.org/W3158456367"],"related_works":["https://openalex.org/W4379115841","https://openalex.org/W2083794993","https://openalex.org/W1511772879","https://openalex.org/W4394660363","https://openalex.org/W2186315912","https://openalex.org/W2248125223","https://openalex.org/W2588591308","https://openalex.org/W2127898439","https://openalex.org/W3082894236","https://openalex.org/W3195664246"],"abstract_inverted_index":{"Bayesian":[0,33,150],"deep":[1,6,23,34,151],"learning":[2,24,35,152],"seeks":[3],"to":[4,12,21,45,88,122,127,145],"equip":[5],"neural":[7],"networks":[8],"with":[9],"the":[10,56,72,90,182],"ability":[11],"precisely":[13],"quantify":[14],"their":[15],"predictive":[16,93,138],"uncertainty,":[17],"and":[18,85,124,148,165,170,199],"has":[19],"promised":[20],"make":[22],"more":[25],"reliable":[26,67,137],"for":[27,163],"safety-critical":[28,96],"real-world":[29,60,78],"applications.":[30],"Yet,":[31],"existing":[32],"methods":[36,43,153,179],"fall":[37],"short":[38],"of":[39,58,77,92,105,113,131,177],"this":[40],"promise;":[41],"new":[42],"continue":[44],"be":[46],"evaluated":[47],"on":[48,154,201],"unrealistic":[49],"test":[50],"beds":[51],"that":[52,62,80,119,135],"do":[53],"not":[54],"reflect":[55,82],"complexities":[57,84],"downstream":[59],"tasks":[61,79,134,144],"would":[63],"benefit":[64],"most":[65],"from":[66],"uncertainty":[68,139],"quantification.":[69,140],"We":[70,141,158,174],"propose":[71],"RETINA":[73],"Benchmark,":[74],"a":[75,116,129],"set":[76],"accurately":[81],"such":[83],"are":[86],"designed":[87],"assess":[89],"reliability":[91],"models":[94],"in":[95,181],"scenarios.":[97],"Specifically,":[98],"we":[99],"curate":[100],"two":[101],"publicly":[102],"available":[103],"datasets":[104],"high-resolution":[106],"human":[107],"retina":[108],"images":[109],"exhibiting":[110],"varying":[111],"degrees":[112],"diabetic":[114],"retinopathy,":[115],"medical":[117],"condition":[118],"can":[120],"lead":[121],"blindness,":[123],"use":[125,142],"them":[126],"design":[128,172],"suite":[130],"automated":[132],"diagnosis":[133],"require":[136],"these":[143],"benchmark":[146,183],"well-established":[147],"state-of-the-art":[149],"task-specific":[155],"evaluation":[156,200],"metrics.":[157],"provide":[159,175],"an":[160],"easy-to-use":[161],"codebase":[162],"fast":[164],"easy":[166],"benchmarking":[167],"following":[168],"reproducibility":[169],"software":[171],"principles.":[173],"implementations":[176],"all":[178],"included":[180],"as":[184,186],"well":[185],"results":[187],"computed":[188],"over":[189],"100":[190],"TPU":[191],"days,":[192,195],"20":[193],"GPU":[194],"400":[196],"hyperparameter":[197],"configurations,":[198],"at":[202],"least":[203],"6":[204],"random":[205],"seeds":[206],"each.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2021-10-11T00:00:00"}
