{"id":"https://openalex.org/W7161755537","doi":"https://doi.org/10.1109/isbi61048.2026.11515555","title":"Uncertainty-Aware Image Classification in Biomedical Imaging Using Spectral-Normalized Neural Gaussian Processes","display_name":"Uncertainty-Aware Image Classification in Biomedical Imaging Using Spectral-Normalized Neural Gaussian Processes","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161755537","doi":"https://doi.org/10.1109/isbi61048.2026.11515555"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515555","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/A5133729309","display_name":"Uma Meleti","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Uma Meleti","raw_affiliation_strings":["University of Wisconsin-Madison,Department of Pathology and Lab Medicine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison,Department of Pathology and Lab Medicine","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012415712","display_name":"Jeffrey Nirschl","orcid":"https://orcid.org/0000-0001-6857-341X"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeffrey J. Nirschl","raw_affiliation_strings":["University of Wisconsin-Madison,Department of Pathology and Lab Medicine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison,Department of Pathology and Lab Medicine","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5133729309"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93613541,"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":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.8718000054359436,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.8718000054359436,"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":0.0357000008225441,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.00559999980032444,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5751000046730042},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5074999928474426},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3950999975204468},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.36970001459121704},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.31349998712539673},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.30169999599456787}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7050999999046326},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5751000046730042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.545799970626831},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5074999928474426},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37630000710487366},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.36970001459121704},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.30169999599456787},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.25999999046325684}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515555","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":7,"referenced_works":["https://openalex.org/W2435090885","https://openalex.org/W2795587190","https://openalex.org/W2898477488","https://openalex.org/W2964059111","https://openalex.org/W3015930014","https://openalex.org/W4225324714","https://openalex.org/W4280624090"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"histopathologic":[1],"interpretation":[2],"is":[3],"key":[4],"for":[5,13,130],"clinical":[6,30],"decision-making;":[7],"however,":[8],"current":[9],"deep":[10],"learning":[11],"models":[12,125],"digital":[14,134],"pathology":[15],"are":[16],"often":[17],"overconfident":[18],"and":[19,29,64,80,88,105,118,139],"poorly":[20],"calibrated":[21],"in":[22,133],"out-of-distribution":[23],"(OOD)":[24],"settings,":[25],"which":[26],"limit":[27],"trust":[28,141],"adoption.":[31],"Safety-critical":[32],"medical":[33],"imaging":[34],"workflows":[35],"benefit":[36],"from":[37],"intrinsic":[38],"uncertainty-aware":[39,131],"properties":[40],"that":[41,60],"can":[42],"accurately":[43],"reject":[44],"OOD":[45,81,119],"input.":[46],"We":[47,83],"implement":[48],"the":[49,66],"Spectral-normalized":[50],"Neural":[51],"Gaussian":[52,72],"Process":[53],"(SNGP),":[54],"a":[55,71,127],"set":[56],"of":[57],"lightweight":[58],"modifications":[59],"apply":[61],"spectral":[62],"normalization":[63],"replace":[65],"final":[67],"dense":[68],"layer":[69,74],"with":[70,142],"process":[73],"to":[75],"improve":[76],"single-model":[77],"uncertainty":[78,116],"estimation":[79,117],"detection.":[82,120],"evaluate":[84],"SNGP":[85,108,122],"vs.":[86],"deterministic":[87],"Monte":[89],"Carlo":[90],"dropout":[91],"on":[92],"six":[93],"datasets":[94],"across":[95],"three":[96],"biomedical":[97],"classification":[98,132],"tasks:":[99],"white":[100],"blood":[101],"cells,":[102],"amyloid":[103],"plaques,":[104],"colorectal":[106],"histopathology.":[107],"has":[109],"comparable":[110],"in-distribution":[111],"performance":[112],"while":[113],"significantly":[114],"improving":[115],"Thus,":[121],"or":[123],"related":[124],"offer":[126],"useful":[128],"framework":[129],"pathology,":[135],"supporting":[136],"safe":[137],"deployment":[138],"building":[140],"pathologists.":[143]},"counts_by_year":[],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2026-05-21T00:00:00"}
