{"id":"https://openalex.org/W7147624243","doi":"https://doi.org/10.48550/arxiv.2603.26726","title":"A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data","display_name":"A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7147624243","doi":"https://doi.org/10.48550/arxiv.2603.26726"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26726","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26726","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.2603.26726","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092081420","display_name":"Aram Ansary Ogholbake","orcid":"https://orcid.org/0009-0008-7985-6309"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ogholbake, Aram Ansary","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047888639","display_name":"Hannah L. Choi","orcid":"https://orcid.org/0000-0003-0556-8351"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Hannah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Brandenburg, Spencer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brandenburg, Spencer","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126192257","display_name":"Alyssa Antuna","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antuna, Alyssa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070615441","display_name":"Zahraa F. Al-Sharshahi","orcid":"https://orcid.org/0000-0003-3601-8467"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Al-Sharshahi, Zahraa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Cox, Makayla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cox, Makayla","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132718135","display_name":"Haseeb Ahmed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Haseeb","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058262905","display_name":"Jacqueline A. Frank","orcid":"https://orcid.org/0000-0002-3216-518X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frank, Jacqueline","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104943910","display_name":"N Millson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Millson, Nathan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130714672","display_name":"Luke X. Bauerle","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bauerle, Luke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132687786","display_name":"Jessica C. Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jessica","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054945129","display_name":"David Dornbos","orcid":"https://orcid.org/0000-0002-0039-0016"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dornbos, David","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132681776","display_name":"Qiang Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Qiang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"corresponding_author_ids":["https://openalex.org/A5092081420"],"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.13699999451637268,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.13699999451637268,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10227","display_name":"Acute Ischemic Stroke Management","score":0.09700000286102295,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T11763","display_name":"Intracerebral and Subarachnoid Hemorrhage Research","score":0.0575999990105629,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6833000183105469},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.6133000254631042},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5457000136375427},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5138000249862671},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4932999908924103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4156000018119812},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.413100004196167},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.40939998626708984},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3862000107765198}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7501999735832214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6833000183105469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6527000069618225},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6133000254631042},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4932999908924103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4632999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.413100004196167},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.337799996137619},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.323199987411499},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26726","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26726","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.2603.26726","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26726","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":{"We":[0,167],"propose":[1],"AttentionMixer,":[2],"a":[3,59,70,97,157,171],"unified":[4],"deep":[5],"learning":[6],"framework":[7],"for":[8,127],"multimodal":[9,128,190,232],"detection":[10,238],"of":[11,209],"brain":[12,173],"edema":[13,178,237],"that":[14,43,229],"combines":[15],"structural":[16],"head":[17],"CT":[18],"(HCT)":[19],"with":[20,145,176,184],"routine":[21],"clinical":[22,30,164,240],"metadata.":[23],"While":[24],"HCT":[25,64,174],"provides":[26,123],"rich":[27],"spatial":[28],"information,":[29],"variables":[31,223],"such":[32],"as":[33,92,105],"age,":[34],"laboratory":[35],"values,":[36],"and":[37,61,90,94,122,188,212,215],"scan":[38],"timing":[39],"capture":[40],"complementary":[41],"context":[42,121],"might":[44],"be":[45],"ignored":[46],"or":[47,151],"naively":[48],"concatenated.":[49],"AttentionMixer":[50,169,192],"is":[51],"designed":[52],"to":[53,113,162],"fuse":[54],"these":[55],"heterogeneous":[56],"sources":[57],"in":[58,96,239],"principled":[60],"efficient":[62],"manner.":[63],"volumes":[65],"are":[66,83,154],"first":[67],"encoded":[68],"using":[69,180],"self-supervised":[71],"Vision":[72],"Transformer":[73],"Autoencoder":[74],"(ViT-AE++),":[75],"without":[76],"requiring":[77],"large":[78],"labeled":[79],"datasets.":[80],"Clinical":[81],"metadata":[82,153,217],"mapped":[84],"into":[85],"the":[86,111,135,207],"same":[87],"feature":[88,102],"space":[89],"used":[91],"keys":[93],"values":[95],"cross-attention":[98,108,211],"module,":[99],"where":[100],"HCT-derived":[101],"vector":[103],"serves":[104],"queries.":[106],"This":[107],"fusion":[109,233],"allows":[110],"network":[112],"dynamically":[114],"modulate":[115],"imaging":[116],"features":[117],"based":[118],"on":[119,170],"patient-specific":[120],"an":[124],"interpretable":[125,231],"mechanism":[126],"integration.":[129],"A":[130],"lightweight":[131],"MLP-Mixer":[132,213],"then":[133],"refines":[134],"fused":[136],"representation":[137],"before":[138],"final":[139],"classification,":[140],"enabling":[141],"global":[142],"dependency":[143],"modeling":[144],"substantially":[146,235],"reduced":[147],"parameter":[148],"overhead.":[149],"Missing":[150],"incomplete":[152],"handled":[155],"via":[156],"learnable":[158],"embedding,":[159],"promoting":[160],"robustness":[161],"real-world":[163],"data":[165],"quality.":[166],"evaluate":[168],"curated":[172],"cohort":[175],"expert":[177],"annotations":[179],"five-fold":[181],"cross-validation.":[182],"Compared":[183],"strong":[185],"HCT-only,":[186],"metadata-only,":[187],"prior":[189],"baselines,":[191],"achieves":[193],"superior":[194],"performance":[195],"(accuracy":[196],"87.32%,":[197],"precision":[198],"92.10%,":[199],"F1-score":[200],"85.37%,":[201],"AUC":[202],"94.14%).":[203],"Ablation":[204],"studies":[205],"confirm":[206],"benefit":[208],"both":[210],"refinement,":[214],"permutation-based":[216],"importance":[218],"analysis":[219],"highlights":[220],"clinically":[221],"meaningful":[222],"driving":[224],"predictions.":[225],"These":[226],"results":[227],"demonstrate":[228],"structured,":[230],"can":[234],"improve":[236],"practice.":[241]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-04-02T00:00:00"}
