{"id":"https://openalex.org/W7160903906","doi":"https://doi.org/10.48550/arxiv.2605.09151","title":"MultiMedVision: Multi-Modal Medical Vision Framework","display_name":"MultiMedVision: Multi-Modal Medical Vision Framework","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160903906","doi":"https://doi.org/10.48550/arxiv.2605.09151"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09151","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09151","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.2605.09151","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042209918","display_name":"Frank Li","orcid":"https://orcid.org/0000-0002-8205-3397"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Frank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041981029","display_name":"Bardia Khosravi","orcid":"https://orcid.org/0000-0002-8024-339X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khosravi, Bardia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009060599","display_name":"Mohammadreza Chavoshi","orcid":"https://orcid.org/0000-0001-6598-7190"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chavoshi, Mohammadreza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031359624","display_name":"Young Seok Jeon","orcid":"https://orcid.org/0000-0002-4948-083X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeon, Young Seok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121229148","display_name":"Theo Dapamede","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dapamede, Theo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135444295","display_name":"Hari Trivedi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trivedi, Hari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135839157","display_name":"Janice Newsome","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Newsome, Janice","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135993389","display_name":"Judy Gichoya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gichoya, Judy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5042209918"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.4641000032424927,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4641000032424927,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.20679999887943268,"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":0.12680000066757202,"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/process","display_name":"Process (computing)","score":0.6028000116348267},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5954999923706055},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5950000286102295},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5630000233650208},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4984000027179718},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46889999508857727},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4375},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.3846000134944916},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3817000091075897}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7192999720573425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6565999984741211},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5950000286102295},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5630000233650208},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4984000027179718},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46630001068115234},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4375},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35269999504089355},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09151","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09151","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.2605.09151","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09151","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":{"Multi-modal":[0],"medical":[1],"imaging":[2],"enables":[3],"comprehensive":[4],"diagnostics,":[5],"yet":[6],"current":[7],"foundation":[8],"models":[9],"process":[10,52],"2D":[11,69,101],"(e.g.":[12,16],"X-ray)":[13],"and":[14,47,81,85,111,125],"3D":[15,43,66,112],"CT)":[17],"data":[18],"with":[19,73,91],"separate,":[20],"dimensionality-specific":[21],"architectures.":[22],"We":[23],"present":[24],"MultiMedVision,":[25],"a":[26,36,57,74,87],"unified":[27,131],"framework":[28],"for":[29],"joint":[30],"2D/3D":[31],"representation":[32,133],"learning":[33,134],"built":[34],"on":[35,77,99,106,109,115],"Sparse":[37],"Vision":[38],"Transformer.":[39],"Our":[40],"model":[41],"uses":[42],"Rotary":[44],"Positional":[45],"Embeddings":[46],"variable-length":[48],"sequence":[49],"packing":[50],"to":[51],"mixed-modality":[53],"batches":[54],"natively":[55],"within":[56],"shared":[58,89,126],"latent":[59],"space,":[60],"without":[61,137],"modality-specific":[62,124,139],"adapters":[63],"or":[64],"treating":[65],"volumes":[67],"as":[68],"slice":[70],"sequences.":[71],"Trained":[72],"self-supervised":[75],"objective":[76],"chest":[78],"X-rays":[79],"(MIMIC-CXR)":[80],"CT":[82],"scans":[83],"(CT-RATE),":[84],"using":[86],"single":[88],"encoder":[90],"5x":[92],"less":[93],"data,":[94],"MultiMedVision":[95],"achieves":[96],"competitive":[97],"performance":[98],"both":[100],"benchmarks":[102],"(Macro":[103],"AUROC":[104],"0.82":[105],"MIMIC,":[107],"0.84":[108],"CheXpert)":[110],"tasks":[113],"(0.85":[114],"CT-RATE).":[116],"Analysis":[117],"of":[118],"the":[119],"learned":[120],"representations":[121],"reveals":[122],"coexisting":[123],"feature":[127],"subspaces,":[128],"demonstrating":[129],"that":[130],"cross-dimensional":[132],"is":[135],"feasible":[136],"sacrificing":[138],"performance.":[140]},"counts_by_year":[],"updated_date":"2026-05-13T06:11:35.469786","created_date":"2026-05-13T00:00:00"}