{"id":"https://openalex.org/W7134961853","doi":"https://doi.org/10.48550/arxiv.2603.09109","title":"VIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs","display_name":"VIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134961853","doi":"https://doi.org/10.48550/arxiv.2603.09109"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09109","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.09109","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128713419","display_name":"Xiyao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Xiyao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128761159","display_name":"Xiaoyu Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Xiaoyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025440962","display_name":"Yang Dai","orcid":"https://orcid.org/0000-0002-7638-849X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Yang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128795301","display_name":"Yuxuan Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Yuxuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128778502","display_name":"Shuo Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shuo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122520371","display_name":"Xihe Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Xihe","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5128713419"],"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.4050999879837036,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.4050999879837036,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.19939999282360077,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1670999974012375,"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/json","display_name":"JSON","score":0.5989999771118164},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5489000082015991},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4526999890804291},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.41350001096725464},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3637999892234802},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.34310001134872437},{"id":"https://openalex.org/keywords/semantic-mapping","display_name":"Semantic mapping","score":0.3278000056743622}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.708899974822998},{"id":"https://openalex.org/C2780416260","wikidata":"https://www.wikidata.org/wiki/Q2063","display_name":"JSON","level":2,"score":0.5989999771118164},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5489000082015991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51910001039505},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.41350001096725464},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3529999852180481},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2921000123023987},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25690001249313354},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2563999891281128},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25279998779296875},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09109","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.09109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09109","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"pretraining":[1],"has":[2],"driven":[3],"significant":[4],"progress":[5],"in":[6,185],"medical":[7,59],"image":[8],"analysis.":[9],"However,":[10],"current":[11],"methods":[12],"typically":[13],"supervise":[14],"visual":[15,100],"encoders":[16],"using":[17,136],"one-hot":[18],"labels":[19],"or":[20],"free-form":[21],"text,":[22],"neither":[23],"of":[24,128],"which":[25],"effectively":[26],"captures":[27],"the":[28,103],"complex":[29],"semantic":[30,55],"relationships":[31],"among":[32],"clinical":[33,65,186],"findings.":[34],"In":[35],"this":[36],"study,":[37],"we":[38],"introduce":[39],"VIVID-Med,":[40],"a":[41,46,53,73,109,126,175],"novel":[42],"framework":[43],"that":[44],"leverages":[45],"frozen":[47],"large":[48],"language":[49],"model":[50],"(LLM)":[51],"as":[52],"structured":[54],"teacher":[56],"to":[57,81,91,147,156,180],"pretrain":[58],"vision":[60],"transformers":[61],"(ViTs).":[62],"VIVID-Med":[63,116,173],"translates":[64],"findings":[66],"into":[67,94],"verifiable":[68],"JSON":[69],"field-state":[70],"pairs":[71],"via":[72],"Unified":[74],"Medical":[75],"Schema":[76],"(UMS),":[77],"utilizing":[78],"answerability-aware":[79],"masking":[80],"focus":[82],"optimization.":[83],"It":[84,140],"then":[85],"employs":[86],"Structured":[87],"Prediction":[88],"Decomposition":[89],"(SPD)":[90],"partition":[92],"cross-attention":[93],"orthogonality-regularized":[95],"query":[96],"groups,":[97],"extracting":[98],"complementary":[99],"aspects.":[101],"Crucially,":[102],"LLM":[104],"is":[105],"discarded":[106],"post-training,":[107],"yielding":[108],"lightweight,":[110],"deployable":[111],"ViT-only":[112],"backbone.":[113],"We":[114],"evaluated":[115],"across":[117],"multiple":[118],"settings:":[119],"on":[120,161,169],"CheXpert":[121],"linear":[122],"probing,":[123],"it":[124],"achieves":[125],"macro-AUC":[127,168],"0.8588,":[129],"outperforming":[130],"BiomedCLIP":[131],"by":[132],"+6.65":[133],"points":[134],"while":[135],"500x":[137],"less":[138],"data.":[139],"also":[141],"demonstrates":[142],"robust":[143],"zero-shot":[144],"cross-domain":[145],"transfer":[146],"NIH":[148],"ChestX-ray14":[149],"(0.7225":[150],"macro-AUC)":[151],"and":[152,166],"strong":[153],"cross-modality":[154],"generalization":[155],"CT,":[157],"achieving":[158],"0.8413":[159],"AUC":[160],"LIDC-IDRI":[162],"lung":[163],"nodule":[164],"classification":[165],"0.9969":[167],"OrganAMNIST":[170],"11-organ":[171],"classification.":[172],"offers":[174],"highly":[176],"efficient,":[177],"scalable":[178],"alternative":[179],"deploying":[181],"resource-heavy":[182],"vision-language":[183],"models":[184],"settings.":[187]},"counts_by_year":[],"updated_date":"2026-03-12T06:18:43.230356","created_date":"2026-03-12T00:00:00"}