{"id":"https://openalex.org/W7164937559","doi":"https://doi.org/10.48550/arxiv.2606.16484","title":"Unified Multimodal Model for Brain MRI Imputation and Understanding","display_name":"Unified Multimodal Model for Brain MRI Imputation and Understanding","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164937559","doi":"https://doi.org/10.48550/arxiv.2606.16484"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16484","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16484","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138727796","display_name":"Zhiyun Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Zhiyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138696200","display_name":"Che Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Che","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138721260","display_name":"Tian Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034120908","display_name":"Avinash Kori","orcid":"https://orcid.org/0000-0002-5878-3584"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kori, Avinash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138696662","display_name":"Wenjia Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Wenjia","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.28949999809265137,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.28949999809265137,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.10719999670982361,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.09189999848604202,"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/leverage","display_name":"Leverage (statistics)","score":0.5861999988555908},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5067999958992004},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4812000095844269},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.46140000224113464},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4246000051498413},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.41499999165534973},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41280001401901245},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4059999883174896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7322999835014343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6873000264167786},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5861999988555908},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5067999958992004},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4812000095844269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4641000032424927},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.3781000077724457},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C2991673738","wikidata":"https://www.wikidata.org/wiki/Q5062122","display_name":"Brain disease","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16484","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16484","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7189390659332275,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"large":[1],"language":[2],"models":[3],"(MLLMs)":[4],"hold":[5],"great":[6],"potential":[7,78],"for":[8,69,174],"medicine,":[9],"as":[10],"they":[11],"inherit":[12],"knowledge":[13],"from":[14],"LLM":[15],"and":[16,25,48,95,103,179],"allow":[17],"multiple":[18],"data":[19,47,54,105],"modalities":[20],"to":[21,89,109,129,134,152],"be":[22],"integrated,":[23],"analysed":[24],"interpreted":[26],"in":[27,55,113],"natural":[28],"language.":[29],"However,":[30],"the":[31,42,49,56,111,154],"field":[32],"of":[33,44,52,185],"medical":[34,118],"MLLMs":[35],"is":[36,107,127],"constrained":[37],"by":[38],"non-trivial":[39],"challenges,":[40],"notably":[41],"scarcity":[43],"high-quality":[45],"training":[46,87],"frequent":[50],"occurrence":[51],"missing":[53,79],"real-world":[57],"clinical":[58],"setting.":[59],"Here,":[60],"we":[61,83,145],"propose":[62,146],"a":[63,85,147],"novel":[64],"unified":[65,86],"multimodal":[66,122,159],"model,":[67],"UniBrain,":[68],"brain":[70,80,96,165,175],"magnetic":[71],"resonance":[72],"image":[73,97,132,142,176],"(MRI)":[74],"analysis.":[75],"To":[76],"address":[77],"MRI":[81,166],"modalities,":[82],"employ":[84],"strategy":[88,126],"perform":[90],"joint":[91],"imaging":[92],"modality":[93,186],"imputation":[94],"understanding.":[98],"During":[99],"training,":[100],"an":[101,114],"interleaved":[102],"description-enriched":[104],"flow":[106],"constructed":[108],"train":[110],"model":[112],"autoregressive":[115],"manner,":[116],"enabling":[117],"reasoning":[119],"with":[120],"generated":[121],"data.":[123],"A":[124],"self-alignment":[125],"introduced":[128],"leverage":[130],"dense":[131],"embeddings":[133],"learn":[135],"fine-grained":[136],"anatomical":[137],"features":[138],"without":[139],"requiring":[140],"detailed":[141],"captions.":[143],"Furthermore,":[144],"dynamic":[148],"hidden":[149],"state":[150],"mechanism":[151],"alleviate":[153],"exposure":[155],"bias":[156],"during":[157],"long-context":[158],"inference.":[160],"Extensive":[161],"experiments":[162],"on":[163],"multi-disease":[164],"dataset":[167],"demonstrate":[168],"that":[169],"UniBrain":[170],"achieves":[171],"high":[172],"performance":[173],"imputation,":[177],"understanding,":[178],"disease":[180],"diagnosis":[181],"under":[182],"various":[183],"extents":[184],"incompleteness.":[187]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}