{"id":"https://openalex.org/W7154239163","doi":"https://doi.org/10.48550/arxiv.2604.10233","title":"Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis","display_name":"Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis","publication_year":2026,"publication_date":"2026-04-11","ids":{"openalex":"https://openalex.org/W7154239163","doi":"https://doi.org/10.48550/arxiv.2604.10233"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.10233","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10233","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.10233","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133562176","display_name":"Yang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101204537","display_name":"Dunyuan Xu","orcid":"https://orcid.org/0000-0001-7600-9384"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Dunyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070831420","display_name":"Yaoqian Li","orcid":"https://orcid.org/0000-0002-5189-8151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yaoqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133621377","display_name":"Xiaomeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaomeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133621320","display_name":"Jinpeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinpeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133554089","display_name":"Pheng-Ann Heng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heng, Pheng-Ann","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":0.9340000152587891,"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":0.9340000152587891,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.020400000736117363,"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/T10028","display_name":"Topic Modeling","score":0.010900000110268593,"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/encoder","display_name":"Encoder","score":0.6446999907493591},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5392000079154968},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4447999894618988},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.44130000472068787},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.4129999876022339},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3955000042915344},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.3828999996185303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7730000019073486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6782000064849854},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6446999907493591},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.517300009727478},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4447999894618988},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.44130000472068787},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3955000042915344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38510000705718994},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3089999854564667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.10233","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10233","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.10233","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10233","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"3D":[0,65,69,111,177],"medical":[1,40,45,66,70,112,178],"image":[2,83,130,168],"analysis":[3],"is":[4,102,194],"of":[5,39,64,88,118,151],"great":[6,33],"importance":[7],"in":[8,56,180],"disease":[9],"diagnosis":[10],"and":[11,27,44,78,166,183],"treatment.":[12],"Recently,":[13],"multimodal":[14],"large":[15],"language":[16],"models":[17],"(MLLMs)":[18],"have":[19,32],"exhibited":[20],"robust":[21],"perceptual":[22],"capacity,":[23],"strong":[24],"cross-modal":[25],"alignment,":[26],"promising":[28],"generalizability.":[29],"Therefore,":[30],"they":[31],"potential":[34],"to":[35,61,80,95,109,127,162],"improve":[36],"the":[37,62,124,149,152],"performance":[38],"report":[41],"generation":[42],"(MRG)":[43],"visual":[46],"question":[47],"answering":[48],"(MVQA),":[49],"which":[50,101,144],"serve":[51],"as":[52],"two":[53],"important":[54],"tasks":[55,147],"clinical":[57],"scenarios.":[58],"However,":[59],"due":[60],"scarcity":[63],"images,":[67,108],"existing":[68,176],"MLLMs":[71,179],"suffer":[72],"from":[73],"insufficiently":[74],"pretrained":[75],"vision":[76,125],"encoder":[77,126],"inability":[79],"extract":[81,128],"customized":[82],"features":[84,131],"for":[85,132],"different":[86],"kinds":[87],"tasks.":[89,185],"In":[90],"this":[91,192],"paper,":[92],"we":[93,135,156],"propose":[94,157],"first":[96],"transfer":[97],"a":[98,138,158],"2D":[99,106],"MLLM,":[100],"well":[103],"trained":[104],"with":[105],"natural":[107],"support":[110],"volumetric":[113],"inputs":[114],"while":[115],"reusing":[116],"all":[117],"its":[119],"pre-trained":[120],"parameters.":[121],"To":[122],"enable":[123],"tailored":[129],"various":[133],"tasks,":[134],"then":[136],"design":[137],"Text-Guided":[139],"Hierarchical":[140],"MoE":[141],"(TGH-MoE)":[142],"framework,":[143],"can":[145],"distinguish":[146],"under":[148],"guidance":[150],"text":[153],"prompt.":[154],"Furthermore,":[155],"two-stage":[159],"training":[160],"strategy":[161],"learn":[163],"both":[164,181],"task-shared":[165],"task-specific":[167],"features.":[169],"As":[170],"demonstrated":[171],"empirically,":[172],"our":[173],"method":[174],"outperforms":[175],"MRG":[182],"MVQA":[184],"Our":[186],"code":[187],"will":[188],"be":[189],"released":[190],"once":[191],"paper":[193],"accepted.":[195]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}