{"id":"https://openalex.org/W4414360603","doi":"https://doi.org/10.24963/ijcai.2025/201","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","display_name":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360603","doi":"https://doi.org/10.24963/ijcai.2025/201"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/201","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036448040","display_name":"Mingcheng Qu","orcid":"https://orcid.org/0000-0002-5627-4567"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingcheng Qu","raw_affiliation_strings":["Harbin Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032001329","display_name":"Guang Yang","orcid":"https://orcid.org/0000-0001-8942-427X"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Yang","raw_affiliation_strings":["Harbin Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031208819","display_name":"Donglin Di","orcid":"https://orcid.org/0000-0002-2270-3378"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donglin Di","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033324841","display_name":"Tonghua Su","orcid":"https://orcid.org/0000-0002-8869-1664"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tonghua Su","raw_affiliation_strings":["Harbin Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101600821","display_name":"Yue Gao","orcid":"https://orcid.org/0000-0001-8404-6003"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Gao","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041567418","display_name":"Yang Song","orcid":"https://orcid.org/0000-0003-1283-1672"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yang Song","raw_affiliation_strings":["University of New South Wales"],"affiliations":[{"raw_affiliation_string":"University of New South Wales","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100640159","display_name":"Lei Fan","orcid":"https://orcid.org/0000-0002-9988-4517"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lei Fan","raw_affiliation_strings":["University of New South Wales"],"affiliations":[{"raw_affiliation_string":"University of New South Wales","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5036448040"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":7.405,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.9699287,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1802","last_page":"1810"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9402999877929688,"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/T10862","display_name":"AI in cancer detection","score":0.9402999877929688,"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/granularity","display_name":"Granularity","score":0.5935999751091003},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.5401999950408936},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5087000131607056},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4717999994754791},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4562999904155731},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.35679998993873596},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.3345000147819519}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7060999870300293},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5759000182151794},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.5401999950408936},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5087000131607056},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4932999908924103},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C189206191","wikidata":"https://www.wikidata.org/wiki/Q222046","display_name":"Genomics","level":4,"score":0.3192000091075897},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C19443361","wikidata":"https://www.wikidata.org/wiki/Q5282533","display_name":"Disparate system","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C2778058735","wikidata":"https://www.wikidata.org/wiki/Q4692253","display_name":"Aggregate data","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/201","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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],"pathology-genomic":[1],"analysis":[2],"has":[3],"become":[4],"increasingly":[5],"prominent":[6],"in":[7,37,57,68,143],"cancer":[8],"survival":[9,78],"prediction.":[10],"However,":[11],"existing":[12],"studies":[13],"mainly":[14],"utilize":[15],"multi-instance":[16],"learning":[17,84],"to":[18,47,85,109],"aggregate":[19],"patch-level":[20],"features,":[21],"neglecting":[22],"the":[23,35,112,115,120],"information":[24],"loss":[25],"of":[26,114],"contextual":[27,89],"and":[28,40,44,90,103,124],"hierarchical":[29,91],"details":[30,92],"within":[31],"pathology":[32,43,58,94],"images.":[33,95],"Furthermore,":[34],"disparity":[36],"data":[38,59],"granularity":[39],"dimensionality":[41],"between":[42],"genomics":[45,67],"leads":[46],"a":[48,62,76,99],"significant":[49],"modality":[50,100],"imbalance.":[51,122],"The":[52],"high":[53],"spatial":[54],"resolution":[55],"inherent":[56],"renders":[60],"it":[61,97],"dominant":[63],"role":[64],"while":[65],"overshadowing":[66],"multimodal":[69,77],"integration.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74],"propose":[75],"prediction":[79],"framework":[80],"that":[81,134],"incorporates":[82],"hypergraph":[83],"effectively":[86],"capture":[87],"both":[88],"from":[93],"Moreover,":[96],"employs":[98],"rebalance":[101],"mechanism":[102],"an":[104],"interactive":[105],"alignment":[106],"fusion":[107],"strategy":[108],"dynamically":[110],"reweight":[111],"contributions":[113],"two":[116],"modalities,":[117],"thereby":[118],"mitigating":[119],"pathology-genomics":[121],"Quantitative":[123],"qualitative":[125],"experiments":[126],"are":[127],"conducted":[128],"on":[129],"five":[130],"TCGA":[131],"datasets,":[132],"demonstrating":[133],"our":[135],"model":[136],"outperforms":[137],"advanced":[138],"methods":[139],"by":[140],"over":[141],"3.4%":[142],"C-Index":[144],"performance.":[145],"Code:":[146],"https://github.com/MCPathology/MRePath.":[147]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}
