{"id":"https://openalex.org/W7139925417","doi":"https://doi.org/10.48550/arxiv.2603.18343","title":"VISTA: Validation-Guided Integration of Spatial and Temporal Foundation Models with Anatomical Decoding for Rare-Pathology VCE Event Detection","display_name":"VISTA: Validation-Guided Integration of Spatial and Temporal Foundation Models with Anatomical Decoding for Rare-Pathology VCE Event Detection","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7139925417","doi":"https://doi.org/10.48550/arxiv.2603.18343"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18343","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18343","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.18343","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048072295","display_name":"B. Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Bo-Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028466277","display_name":"Yu-Fan Lin","orcid":"https://orcid.org/0000-0002-2290-4540"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yu-Fan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130253039","display_name":"Yu-Zhe Pien","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pien, Yu-Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034075233","display_name":"Chia-Ming Lee","orcid":"https://orcid.org/0009-0004-6027-3083"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Chia-Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032964198","display_name":"Fu-En Yang","orcid":"https://orcid.org/0000-0003-0102-7101"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Fu-En","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130241765","display_name":"Yu-Chiang Frank Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yu-Chiang Frank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007305393","display_name":"Chih\u2013Chung Hsu","orcid":"https://orcid.org/0000-0002-2083-4438"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsu, Chih-Chung","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":false,"cited_by_count":0,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.2410999983549118,"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"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.2410999983549118,"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/T11378","display_name":"Gastrointestinal Bleeding Diagnosis and Treatment","score":0.2092999964952469,"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/T10862","display_name":"AI in cancer detection","score":0.05130000039935112,"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/decoding-methods","display_name":"Decoding methods","score":0.7050999999046326},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6759999990463257},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6126000285148621},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.45840001106262207},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45649999380111694},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4059000015258789},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.35659998655319214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343000173568726},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.7050999999046326},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6759999990463257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6554999947547913},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46160000562667847},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18343","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18343","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":"doi:10.48550/arxiv.2603.18343","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18343","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Capsule":[0],"endoscopy":[1],"event":[2,27,44,113,118],"detection":[3,45],"is":[4,23],"challenging":[5],"because":[6],"diagnostically":[7],"relevant":[8],"findings":[9],"are":[10],"sparse,":[11],"visually":[12],"heterogeneous,":[13],"and":[14,65,82,97,111,128,153],"embedded":[15],"in":[16],"long,":[17],"noisy":[18],"video":[19],"streams,":[20],"while":[21,100],"evaluation":[22],"performed":[24],"at":[25],"the":[26,38,101,138,143],"level":[28],"rather":[29],"than":[30],"by":[31,74],"frame":[32],"accuracy":[33],"alone.":[34],"We":[35],"therefore":[36],"formulate":[37],"RARE-VISION":[39],"task":[40],"as":[41],"a":[42,49,75],"metric-aligned":[43],"problem":[46],"instead":[47],"of":[48,151,156],"purely":[50],"frame-wise":[51],"classification":[52],"task.":[53],"Our":[54],"framework":[55],"combines":[56],"two":[57],"complementary":[58,124],"backbones,":[59,125],"EndoFM-LV":[60],"for":[61,68],"local":[62],"temporal":[63,105,130,149,154],"context":[64],"DINOv3":[66],"ViT-L/16":[67],"strong":[69],"frame-level":[70],"visual":[71],"semantics,":[72],"followed":[73],"Diverse":[76],"Head":[77],"Ensemble,":[78],"Validation-Guided":[79],"Hierarchical":[80],"Fusion,":[81],"Anatomy-Aware":[83],"Temporal":[84],"Event":[85],"Decoding.":[86],"The":[87],"fusion":[88],"stage":[89,103],"uses":[90],"validation-derived":[91],"class-wise":[92],"model":[93],"weighting,":[94,96],"backbone":[95],"probability":[98],"calibration,":[99],"decoding":[102,131],"applies":[104],"smoothing,":[106],"anatomical":[107],"constraints,":[108],"threshold":[109],"refinement,":[110],"per-label":[112],"generation":[114],"to":[115,134],"produce":[116],"stable":[117],"predictions.":[119],"Validation":[120],"ablations":[121],"indicate":[122],"that":[123],"validation-guided":[126],"fusion,":[127],"anatomy-aware":[129],"all":[132],"contribute":[133],"event-level":[135],"performance.":[136],"On":[137],"official":[139],"hidden":[140],"test":[141],"set,":[142],"proposed":[144],"method":[145],"achieved":[146],"an":[147],"overall":[148],"mAP@0.5":[150],"0.3530":[152],"mAP@0.95":[155],"0.3235.":[157]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}