{"id":"https://openalex.org/W7140205666","doi":"https://doi.org/10.1016/j.procs.2026.03.109","title":"Comparative Evaluation of MoE and HMoE for Multiclass Classification in VCE Image Analysis","display_name":"Comparative Evaluation of MoE and HMoE for Multiclass Classification in VCE Image Analysis","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140205666","doi":"https://doi.org/10.1016/j.procs.2026.03.109"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2026.03.109","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.109","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2026.03.109","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tiago Costa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiago Costa","raw_affiliation_strings":["University of Tr\u00e1s-os-Montes e Alto Douro, Vila Real, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tr\u00e1s-os-Montes e Alto Douro, Vila Real, Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jos\u00e9 Castro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jos\u00e9 Castro","raw_affiliation_strings":["University of Tr\u00e1s-os-Montes e Alto Douro, Vila Real, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tr\u00e1s-os-Montes e Alto Douro, Vila Real, Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Marta Salgado","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marta Salgado","raw_affiliation_strings":["Centro Hospitalar Universit\u00e1rio de Santo Ant\u00f3nio, Porto, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro Hospitalar Universit\u00e1rio de Santo Ant\u00f3nio, Porto, Portugal","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Ant\u00f3nio Cunha","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ant\u00f3nio Cunha","raw_affiliation_strings":["ALGORITMI Research Centre, Guimar\u00e3es, Portugal","University of Tr\u00e1s-os-Montes e Alto Douro, Vila Real, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ALGORITMI Research Centre, Guimar\u00e3es, Portugal","institution_ids":[]},{"raw_affiliation_string":"University of Tr\u00e1s-os-Montes e Alto Douro, Vila Real, Portugal","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66030534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"278","issue":null,"first_page":"1267","last_page":"1274"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11378","display_name":"Gastrointestinal Bleeding Diagnosis and Treatment","score":0.7454000115394592,"subfield":{"id":"https://openalex.org/subfields/2715","display_name":"Gastroenterology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11378","display_name":"Gastrointestinal Bleeding Diagnosis and Treatment","score":0.7454000115394592,"subfield":{"id":"https://openalex.org/subfields/2715","display_name":"Gastroenterology"},"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.2085999995470047,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.002400000113993883,"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/deep-learning","display_name":"Deep learning","score":0.5508999824523926},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.505299985408783},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4869000017642975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4456999897956848},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.38190001249313354},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3767000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8942000269889832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7301999926567078},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5508999824523926},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.505299985408783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4887000024318695},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4456999897956848},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.38190001249313354},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.3052999973297119},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C3019116470","wikidata":"https://www.wikidata.org/wiki/Q938957","display_name":"Upper gastrointestinal endoscopy","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2026.03.109","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.109","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2026.03.109","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.109","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2025653905","https://openalex.org/W2150884987","https://openalex.org/W4312453657","https://openalex.org/W4386076493","https://openalex.org/W4390874070","https://openalex.org/W4402727337","https://openalex.org/W4404781870","https://openalex.org/W4405867995"],"related_works":[],"abstract_inverted_index":{"Video":[0],"Capsule":[1],"Endoscopy":[2],"(VCE)":[3],"is":[4,27,48,130],"a":[5,12,92,184,206,235,261,278],"pivotal":[6],"technology":[7],"in":[8,67,107,115,144,300,345],"modern":[9],"gastroenterology,":[10],"offering":[11],"non-invasive":[13],"method":[14],"to":[15,57,61,81,99,122,233,268],"visualize":[16],"the":[17,22,30,102,120,209,258,274,294,337],"entire":[18],"small":[19],"bowel.":[20],"However,":[21,86,156],"clinical":[23,338],"application":[24],"of":[25,41,137,171,208,340],"VCE":[26,83,172,193,348],"hampered":[28],"by":[29,148],"extensive":[31],"review":[32],"time":[33],"required,":[34],"as":[35,135,248],"specialists":[36],"must":[37],"manually":[38],"analyze":[39],"thousands":[40],"images":[42,175],"from":[43],"each":[44],"procedure.":[45],"This":[46,110,331],"process":[47],"not":[49,315],"only":[50,316],"laborious":[51],"and":[52,65,128,201,217,223,231,243,254,328,342],"costly":[53],"but":[54,320],"also":[55,321],"prone":[56],"diagnostic":[58],"errors":[59],"due":[60],"fatigue,":[62],"subtle":[63],"abnormalities,":[64],"variability":[66],"interpretation":[68],"across":[69,101],"clinicians.":[70],"To":[71,178],"address":[72],"this":[73,180],"challenge,":[74],"deep":[75,189],"learning":[76,190],"methods":[77,133,314],"have":[78,140],"been":[79],"explored":[80],"automate":[82],"image":[84,194,349],"analysis.":[85],"most":[87],"existing":[88],"approaches":[89],"rely":[90],"on":[91,286,326],"single":[93],"model":[94],"architecture,":[95],"which":[96,212],"often":[97],"fails":[98],"generalize":[100],"broad":[103],"visual":[104],"diversity":[105],"found":[106],"gastrointestinal":[108,215],"imagery.":[109],"limitation":[111],"becomes":[112],"especially":[113],"pronounced":[114],"multiclass":[116,169],"classification":[117,170],"tasks,":[118],"where":[119],"ability":[121],"distinguish":[123],"between":[124],"visually":[125],"similar":[126],"tissues":[127],"lesions":[129],"essential.":[131],"Ensemble-based":[132],"such":[134],"Mixture":[136],"Experts":[138],"(MoE)":[139],"shown":[141],"promising":[142],"results":[143,309],"general":[145],"computer":[146],"vision":[147],"leveraging":[149],"multiple":[150,269],"specialized":[151,291],"models":[152,227,319],"for":[153,168,192,336],"improved":[154],"robustness.":[155],"no":[157],"prior":[158],"work":[159],"has":[160],"investigated":[161],"MoE":[162,165,199,203,253,259,341],"or":[163,173],"Hierarchical":[164,202],"(HMoE)":[166],"architectures":[167],"endoscopic":[174],"more":[176],"broadly.":[177],"explore":[179],"opportunity,":[181],"we":[182,220],"present":[183],"comparative":[185],"framework":[186],"evaluating":[187],"three":[188],"strategies":[191],"classification:":[195],"individual":[196],"models,":[197,296],"flat":[198],"systems,":[200],"architectures.":[204],"Using":[205],"subset":[207],"Kvasir-Capsule":[210],"dataset,":[211],"contains":[213],"12":[214],"tissue":[216],"lesion":[218],"classes,":[219],"first":[221],"train":[222],"evaluate":[224],"four":[225],"backbone":[226],"(InceptionNeXt,":[228],"EfficientViT,":[229],"ConvNeXtV2,":[230],"DeiT3)":[232],"establish":[234],"performance":[236],"baseline.":[237],"The":[238],"two":[239],"best-performing":[240],"architectures,":[241],"ConvNeXtV2":[242,297],"DeiT3,":[244],"are":[245],"then":[246],"used":[247],"expert":[249,270],"backbones":[250],"within":[251],"both":[252],"HMoE":[255,275,295,343],"systems.":[256],"In":[257,272,293],"configuration,":[260],"gating":[262],"network":[263],"assigns":[264],"dynamic":[265],"per-image":[266],"weights":[267],"instances.":[271],"contrast,":[273],"configuration":[276],"constructs":[277],"learned":[279],"binary":[280],"tree":[281],"that":[282,311],"routes":[283],"samples":[284],"based":[285],"class":[287],"similarity":[288],"through":[289],"increasingly":[290],"branches.":[292],"outperformed":[298],"DeiT3":[299,303],"accuracy,":[301],"whereas":[302],"showed":[304],"superior":[305],"routing":[306,329],"accuracy.":[307],"These":[308],"indicate":[310],"expert-driven":[312],"ensemble":[313],"outperform":[317],"standalone":[318],"offer":[322],"complementary":[323],"advantages":[324],"depending":[325],"architecture":[327],"strategy.":[330],"study":[332],"provides":[333],"new":[334],"evidence":[335],"potential":[339],"frameworks":[344],"scalable,":[346],"accurate":[347],"analysis":[350]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}