{"id":"https://openalex.org/W7162312172","doi":"https://doi.org/10.48550/arxiv.2605.23183","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","display_name":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162312172","doi":"https://doi.org/10.48550/arxiv.2605.23183"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23183","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.2605.23183","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136983193","display_name":"Pengfei Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Pengfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136976439","display_name":"Fangjin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Fangjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136943357","display_name":"Wenwen Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Wenwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066400057","display_name":"Yonghuang Wu","orcid":"https://orcid.org/0000-0002-5804-5573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yonghuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081814863","display_name":"Chengqian Zhao","orcid":"https://orcid.org/0000-0003-3860-8161"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Chengqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136987773","display_name":"Feiyu Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Feiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136923677","display_name":"Xuan Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136986191","display_name":"Jinhua Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jinhua","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/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.5289999842643738,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"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/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.5289999842643738,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.2062000036239624,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.12939999997615814,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6728000044822693},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5055999755859375},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.47839999198913574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4478999972343445},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.44359999895095825},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.4142000079154968},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.40639999508857727},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3952000141143799}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883000135421753},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6728000044822693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6425999999046326},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5055999755859375},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.47839999198913574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4478999972343445},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.40639999508857727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.384799987077713},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C2778227246","wikidata":"https://www.wikidata.org/wiki/Q1365309","display_name":"Glioma","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2775000035762787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23183","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.2605.23183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23183","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":"Peace, Justice and strong institutions","score":0.795610785484314,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Contemporary":[0],"glioma":[1,63],"diagnosis":[2,64],"integrates":[3],"molecular":[4],"features":[5,81,120],"with":[6,65],"histopathology":[7],"to":[8,25,32,95,154],"guide":[9],"clinical":[10,14,38,46],"decision-making.":[11],"However,":[12],"in":[13,20],"settings,":[15],"divergent":[16],"imaging":[17,67],"protocols":[18],"result":[19],"incomplete":[21,66],"MRI":[22],"sequences,":[23],"leading":[24],"two":[26,139],"primary":[27],"challenges:":[28],"forcing":[29],"existing":[30],"frameworks":[31],"discard":[33],"a":[34,55,72,92,102,128],"large":[35],"portion":[36],"of":[37,58,131],"data":[39,150],"during":[40],"training":[41,149],"and":[42,87,112,117,138],"consequently":[43],"limiting":[44],"their":[45],"applicability.":[47],"To":[48],"address":[49],"these":[50],"limitations,":[51],"we":[52,70,100],"propose":[53],"GMENet,":[54],"Generative":[56],"Mixture":[57],"Experts":[59,105],"Network":[60],"for":[61,121],"multi-center":[62,129],"sequences.":[68],"Firstly,":[69],"design":[71],"Cross-attention-based":[73],"Gated":[74],"Generation":[75],"Module":[76,107],"that":[77,108,144],"synthesizes":[78],"missing":[79],"sequence":[80],"from":[82,134],"available":[83],"sequences":[84],"via":[85],"cross-attention":[86],"dynamic":[88],"gating":[89],"mechanisms,":[90],"incorporating":[91],"cycle-consistency":[93],"loss":[94],"preserve":[96],"semantic":[97],"integrity.":[98],"Secondly,":[99],"introduce":[101],"Dynamically":[103],"Weighted":[104],"Fusion":[106],"performs":[109],"mixture-of-experts":[110],"interaction":[111],"confidence-aware":[113],"fusion":[114],"over":[115],"original":[116],"synthesized":[118],"dual-sequence":[119],"multi-task":[122],"prediction.":[123],"We":[124],"evaluate":[125],"GMENet":[126,145],"on":[127,164],"cohort":[130],"1,241":[132],"subjects":[133],"four":[135],"in-house":[136],"datasets":[137],"public":[140],"repositories.":[141],"Experiments":[142],"show":[143],"expands":[146],"clinically":[147],"usable":[148],"by":[151],"97\\%,":[152],"relative":[153],"complete-sequence-only":[155],"data.":[156],"Furthermore,":[157],"it":[158],"consistently":[159],"outperforms":[160],"state-of-the-art":[161],"methods":[162],"trained":[163],"complete":[165],"data,":[166],"demonstrating":[167],"improved":[168],"robustness":[169],"under":[170],"cross-center":[171],"distribution":[172],"shifts.":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}
