{"id":"https://openalex.org/W7158163820","doi":"https://doi.org/10.48550/arxiv.2604.25296","title":"Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs","display_name":"Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs","publication_year":2026,"publication_date":"2026-04-28","ids":{"openalex":"https://openalex.org/W7158163820","doi":"https://doi.org/10.48550/arxiv.2604.25296"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.25296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25296","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.2604.25296","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069323421","display_name":"Jianghang Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Jianghang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134830489","display_name":"Haihua Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Haihua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090384487","display_name":"Deli Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Deli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134825346","display_name":"Kai Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134854244","display_name":"Kai Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134844098","display_name":"Jinghao Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Jinghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134849242","display_name":"Zihan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134826048","display_name":"Yuhang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yuhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134827608","display_name":"Liujuan Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Liujuan","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/T13702","display_name":"Machine Learning in Healthcare","score":0.8575000166893005,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.8575000166893005,"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":0.05559999868273735,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.016499999910593033,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5697000026702881},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4869000017642975},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4104999899864197},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4074000120162964},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.35839998722076416},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.3025999963283539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7631000280380249},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5697000026702881},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39570000767707825},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3287000060081482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3066999912261963},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2856999933719635},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C91632574","wikidata":"https://www.wikidata.org/wiki/Q15088675","display_name":"Data curation","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C2985722590","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medical knowledge","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.25296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25296","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.2604.25296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25296","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":[{"display_name":"Quality Education","score":0.7244644165039062,"id":"https://metadata.un.org/sdg/4"}],"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],"have":[5],"shown":[6],"transformative":[7],"potential":[8],"in":[9,184],"medical":[10,44,75,122,158,167,186],"applications,":[11],"yet":[12],"their":[13,173],"performance":[14,183],"is":[15],"hindered":[16],"by":[17,27],"conventional":[18],"data":[19,109,119,140],"curation":[20],"strategies":[21],"that":[22,87,111,161],"rely":[23],"on":[24],"coarse-grained":[25],"partitioning":[26],"modality":[28],"or":[29],"department.":[30],"Such":[31],"fragmented":[32],"approaches":[33],"fail":[34],"to":[35,50,77,116,120,132,142,175],"capture":[36],"the":[37,47,103,166],"hierarchical":[38,85],"and":[39,54,94,129,137,146,180],"interconnected":[40],"nature":[41],"of":[42,169],"clinical":[43,178],"knowledge,":[45],"limiting":[46],"models'":[48],"ability":[49,174],"perform":[51],"fine-grained":[52],"recognition":[53],"complex":[55,177],"reasoning.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60,105],"propose":[61,106],"a":[62,79,84,97,125],"novel":[63],"Entity-Centric":[64],"Medical":[65,80],"Data":[66],"Engineering":[67],"framework.":[68],"We":[69],"automatically":[70],"extract":[71],"entities":[72],"from":[73],"authoritative":[74],"literature":[76],"construct":[78],"Entity":[81],"Tree":[82],"(MET),":[83],"structure":[86],"systematically":[88],"encodes":[89],"diseases,":[90],"anatomical":[91],"structures,":[92],"modalities,":[93],"symptoms":[95],"into":[96],"unified":[98],"knowledge":[99],"repository.":[100],"Building":[101],"upon":[102],"MET,":[104],"an":[107],"advanced":[108],"engine":[110],"includes:":[112],"(1)":[113],"node-guided":[114],"retrieval":[115],"anchor":[117],"raw":[118],"specific":[121],"concepts,":[123],"(2)":[124],"two-stage":[126],"hybrid":[127],"filtering":[128],"alignment":[130],"pipeline":[131],"ensure":[133],"precise":[134],"visual-semantic":[135],"correspondence,":[136],"(3)":[138],"knowledge-aware":[139],"synthesis":[141],"generate":[143],"enriched":[144],"captions":[145],"targeted":[147],"reasoning":[148],"VQA":[149],"pairs,":[150],"leveraging":[151],"structural":[152],"constraints.":[153],"Extensive":[154],"evaluations":[155],"across":[156],"six":[157],"benchmarks":[159],"demonstrate":[160],"our":[162],"approach":[163],"significantly":[164],"enhances":[165],"capabilities":[168],"general-purpose":[170],"MLLMs,":[171],"improving":[172],"handle":[176],"queries":[179],"achieve":[181],"state-of-the-art":[182],"diverse":[185],"contexts.":[187]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-30T00:00:00"}