{"id":"https://openalex.org/W2921908940","doi":"https://doi.org/10.1117/12.2513625","title":"Computer-aided detection using non-convolutional neural network Gaussian processes","display_name":"Computer-aided detection using non-convolutional neural network Gaussian processes","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2921908940","doi":"https://doi.org/10.1117/12.2513625","mag":"2921908940"},"language":"en","primary_location":{"id":"doi:10.1117/12.2513625","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2513625","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.osti.gov/biblio/1784193","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010399198","display_name":"Devanshu Agrawal","orcid":"https://orcid.org/0000-0003-3945-3149"},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Devanshu Agrawal","raw_affiliation_strings":["Oak Ridge National Lab. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oak Ridge National Lab. (United States)","institution_ids":["https://openalex.org/I1289243028"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054049458","display_name":"Hong\u2010Jun Yoon","orcid":"https://orcid.org/0000-0002-5450-5878"},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hong-Jun Yoon","raw_affiliation_strings":["Oak Ridge National Lab. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oak Ridge National Lab. (United States)","institution_ids":["https://openalex.org/I1289243028"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014968146","display_name":"Georgia D. Tourassi","orcid":"https://orcid.org/0000-0002-9418-9638"},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgia Tourassi","raw_affiliation_strings":["Oak Ridge National Lab. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oak Ridge National Lab. (United States)","institution_ids":["https://openalex.org/I1289243028"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039044523","display_name":"Jacob Hinkle","orcid":"https://orcid.org/0000-0002-7751-1760"},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob D. Hinkle","raw_affiliation_strings":["Oak Ridge National Lab. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oak Ridge National Lab. (United States)","institution_ids":["https://openalex.org/I1289243028"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1289243028"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"118","issue":null,"first_page":"131","last_page":"131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994000196456909,"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":0.9994000196456909,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9987000226974487,"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":0.9973999857902527,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8245426416397095},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7595102787017822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6746031641960144},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5950667858123779},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5653104782104492},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5333521366119385},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.47951534390449524},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4646795094013214},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45312264561653137},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44248074293136597},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4368099570274353},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3776478171348572},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.331240177154541}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8245426416397095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595102787017822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6746031641960144},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5950667858123779},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5653104782104492},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5333521366119385},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.47951534390449524},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4646795094013214},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45312264561653137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44248074293136597},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4368099570274353},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3776478171348572},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.331240177154541},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1117/12.2513625","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2513625","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","raw_type":"proceedings-article"},{"id":"pmh:oai:osti.gov:1784193","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1784193","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"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":null}],"best_oa_location":{"id":"pmh:oai:osti.gov:1784193","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1784193","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"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":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W391985582","https://openalex.org/W1567512734","https://openalex.org/W2163605009","https://openalex.org/W2597289420","https://openalex.org/W2766678531","https://openalex.org/W2785626633","https://openalex.org/W2963446712","https://openalex.org/W3101156210","https://openalex.org/W4212774754","https://openalex.org/W4242177601","https://openalex.org/W4295608163","https://openalex.org/W6629804754","https://openalex.org/W6633735129","https://openalex.org/W6674914833","https://openalex.org/W6682631133","https://openalex.org/W6684191040","https://openalex.org/W6684918892","https://openalex.org/W6687483927","https://openalex.org/W6745119210","https://openalex.org/W6745256532","https://openalex.org/W6768888876"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2964954556","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1,155,187],"neural":[2,47,53,124,251],"networks":[3],"(CNNs)":[4],"have":[5],"in":[6,104,119],"recent":[7],"years":[8],"achieved":[9],"record-breaking":[10],"performance":[11,182,218],"on":[12],"many":[13],"image":[14],"classification":[15],"tasks":[16],"and":[17,68,97,138,161,176,186,195],"are":[18,58,229],"therefore":[19,177,196],"well-suited":[20],"for":[21,28,31,36,40,63,238,265],"computer":[22],"aided":[23],"detection":[24],"(CAD).":[25],"The":[26,43],"need":[27,35],"uncertainty":[29,198],"quantification":[30],"CAD":[32],"motivates":[33],"the":[34,51,82,123,142,146,158,181,217,220,261,266,271],"a":[37,93,210],"probabilistic":[38,46],"framework":[39],"deep":[41,117,169],"learning.":[42],"most":[44,235],"well-known":[45],"network":[48,54,66,125,159,252],"model":[49,137,171,191,222,275],"is":[50,73,79,92,121,192,223,234],"Bayesian":[52,194],"(BNN),":[55],"but":[56],"BNNs":[57,85],"notoriously":[59],"difficult":[60],"to":[61,75,116,141,157,179,208],"sample":[62],"large":[64,249,272],"complex":[65],"architectures,":[67],"as":[69,86,258,260],"such":[70],"their":[71,87],"use":[72],"restricted":[74],"small":[76],"problems.":[77],"It":[78],"known":[80],"that":[81,204,216,232,246],"limit":[83],"of":[84,149,219,274],"widths":[88],"increase":[89],"toward":[90],"infinity":[91],"Gaussian":[94,126],"process":[95,127],"(GP),":[96],"there":[98],"has":[99,113],"been":[100,114],"considerable":[101],"research":[102],"interest":[103],"these":[105,239],"infinitely":[106],"wide":[107],"BNNs.":[108],"Recently,":[109],"this":[110,131],"classic":[111],"result":[112],"extended":[115],"architectures":[118],"what":[120],"termed":[122],"(NNGP)":[128],"model.":[129],"In":[130],"work,":[132],"we":[133],"implement":[134],"an":[135,247],"NNGP":[136,170,190,221,262],"apply":[139],"it":[140],"ChestXRay14":[143],"dataset":[144],"at":[145],"full":[147],"resolution":[148],"1024x1024":[150],"pixels.":[151],"Even":[152],"without":[153,162],"any":[154,163],"aspects":[156],"architecture":[160],"data":[164],"augmentation,":[165],"our":[166,243],"five":[167],"layer":[168],"outperforms":[172],"other":[173],"non-convolutional":[174,185],"models":[175],"helps":[178],"narrow":[180],"gap":[183],"between":[184],"models.":[188],"Our":[189],"fully":[193],"offers":[197],"information":[199],"through":[200],"its":[201],"predictive":[202,211],"variance":[203],"can":[205],"be":[206],"used":[207],"formulate":[209],"confidence":[212],"measure.":[213],"We":[214],"show":[215],"significantly":[224],"boosted":[225],"after":[226],"low-confidence":[227,240],"predictions":[228],"rejected,":[230],"suggesting":[231],"convolution":[233],"beneficial":[236],"only":[237],"examples.":[241],"Finally,":[242],"results":[244],"indicate":[245],"extremely":[248],"fully-connected":[250],"with":[253],"appropriate":[254],"regularization":[255],"could":[256],"perform":[257],"well":[259],"if":[263],"not":[264],"computational":[267],"bottleneck":[268],"resulting":[269],"from":[270],"number":[273],"parameters.":[276]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
