{"id":"https://openalex.org/W4417472999","doi":"https://doi.org/10.1109/sipaim67325.2025.11283194","title":"Uncertainty Quantification for Metaplasia Detection in Gastrointestinal Histopathology Images","display_name":"Uncertainty Quantification for Metaplasia Detection in Gastrointestinal Histopathology Images","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W4417472999","doi":"https://doi.org/10.1109/sipaim67325.2025.11283194"},"language":null,"primary_location":{"id":"doi:10.1109/sipaim67325.2025.11283194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim67325.2025.11283194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)","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/A5110949556","display_name":"Ruben Acosta-Velasquez","orcid":null},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Ruben Acosta-Velasquez","raw_affiliation_strings":["Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogotá,Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogotá,Colombia","institution_ids":["https://openalex.org/I36243813"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046258017","display_name":"Fabi\u00e1n Cano","orcid":null},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Fabian Cano","raw_affiliation_strings":["Universidad Nacional de Colombia,Cim@Lab Research Group,Bogotá,Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,Cim@Lab Research Group,Bogotá,Colombia","institution_ids":["https://openalex.org/I36243813"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102790773","display_name":"Eduardo Romero","orcid":"https://orcid.org/0000-0003-2088-2509"},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Eduardo Romero","raw_affiliation_strings":["Universidad Nacional de Colombia,Cim@Lab Research Group,Bogotá,Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,Cim@Lab Research Group,Bogotá,Colombia","institution_ids":["https://openalex.org/I36243813"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080973347","display_name":"Fabio A. Gonz\u00e1lez","orcid":"https://orcid.org/0000-0001-9009-7288"},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Fabio A. Gonz\u00e1lez","raw_affiliation_strings":["Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogotá,Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogotá,Colombia","institution_ids":["https://openalex.org/I36243813"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36243813"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3776299,"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/T10862","display_name":"AI in cancer detection","score":0.8906999826431274,"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/T10862","display_name":"AI in cancer detection","score":0.8906999826431274,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.039500001817941666,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.007199999876320362,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.5274999737739563},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5020999908447266},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4867999851703644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41499999165534973},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.40779998898506165},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.40070000290870667},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.3865000009536743},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3635999858379364}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6399999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.604200005531311},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5274999737739563},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5020999908447266},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.451200008392334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.34599998593330383},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.33880001306533813},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32749998569488525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32600000500679016},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.3059000074863434},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2743000090122223},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sipaim67325.2025.11283194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim67325.2025.11283194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2592929672","https://openalex.org/W2751723768","https://openalex.org/W2760946358","https://openalex.org/W2772723798","https://openalex.org/W2886155082","https://openalex.org/W2899315623","https://openalex.org/W2943370629","https://openalex.org/W2951965145","https://openalex.org/W2956228567","https://openalex.org/W2971561076","https://openalex.org/W2991523350","https://openalex.org/W3015910149","https://openalex.org/W3092280441","https://openalex.org/W3183048323","https://openalex.org/W3211109300","https://openalex.org/W4306648046","https://openalex.org/W4308149258","https://openalex.org/W4390481543"],"related_works":[],"abstract_inverted_index":{"The":[0],"clinical":[1],"adoption":[2],"of":[3,69,130,153],"deep":[4],"learning":[5],"in":[6,71],"computational":[7],"pathology":[8],"requires":[9],"models":[10],"that":[11,107,135],"are":[12,125],"not":[13,108,155],"only":[14,156],"accurate":[15],"but":[16,161],"also":[17,162],"quantify":[18],"the":[19,67,121,150],"uncertainty.":[20],"However,":[21],"a":[22,51,79,91,95,109,164,178],"standardized":[23],"framework":[24],"for":[25,66,157,167,171,174,181],"comparing":[26],"uncertainty":[27],"quantification":[28],"(UQ)":[29],"methods":[30,116],"on":[31,78,94],"challenging":[32],"real-world":[33],"histopathology":[34],"tasks":[35],"is":[36,113],"still":[37],"lacking.":[38],"This":[39],"paper":[40],"addresses":[41],"this":[42],"gap":[43],"by":[44,82],"systematically":[45],"evaluating":[46],"three":[47],"distinct":[48],"UQ":[49,111,154],"strategies":[50],"Bayesian":[52],"neural":[53],"network":[54],"(BNN),":[55],"Monte":[56],"Carlo":[57],"dropout":[58],"(MC-Dropout),":[59],"and":[60,87,170,185],"Kernel":[61],"Density":[62],"Matrix":[63],"(KDM)":[64],"approach":[65,137],"detection":[68],"metaplasia":[70],"gastric":[72],"histology.":[73],"We":[74],"conducted":[75],"our":[76],"analysis":[77],"dataset":[80],"characterized":[81],"significant":[83],"noise,":[84],"staining":[85],"artifacts,":[86],"weak":[88],"annotations,":[89],"using":[90,102],"pipeline":[92],"based":[93],"state-of-the-art":[96],"feature":[97],"extractor.":[98],"Performance":[99],"was":[100],"evaluated":[101],"threshold":[103],"retention":[104],"curves,":[105],"revealing":[106],"single":[110],"paradigm":[112],"superior,":[114],"all":[115],"effectively":[117],"improve":[118],"performance":[119],"as":[120,163],"most":[122],"uncertain":[123],"samples":[124,132],"discarded.":[126],"A":[127],"case":[128],"study":[129],"high-uncertainty":[131],"further":[133],"shows":[134],"any":[136],"robustly":[138],"identifies":[139],"images":[140],"with":[141],"ambiguous":[142],"morphology":[143],"or":[144],"technical":[145],"artifacts.":[146],"These":[147],"findings":[148],"highlight":[149],"practical":[151,179],"utility":[152],"improving":[158],"predictive":[159],"reliability":[160],"critical":[165],"tool":[166],"data":[168],"curation":[169],"triaging":[172],"cases":[173],"expert":[175],"review,":[176],"offering":[177],"guide":[180],"building":[182],"more":[183],"transparent":[184],"trustworthy":[186],"diagnostic":[187],"systems.":[188]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-12-18T00:00:00"}