{"id":"https://openalex.org/W7134164619","doi":"https://doi.org/10.1109/bigdata66926.2025.11401431","title":"A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities","display_name":"A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7134164619","doi":"https://doi.org/10.1109/bigdata66926.2025.11401431"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata66926.2025.11401431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata66926.2025.11401431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/da/publications/f89ef671-789d-40ed-981c-56ad81b23b22","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Michele Zanitti","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Michele Zanitti","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Copenhagen,Denmark"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Vanja Miskovic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vanja Miskovic","raw_affiliation_strings":["Politecnico di Milano,Department of Electronics, Information and Bioengineering,Milan,Italy"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Francesco Trov\u00f2","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francesco Trov\u00f2","raw_affiliation_strings":["Politecnico di Milano,Department of Electronics, Information and Bioengineering,Milan,Italy"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Alessandra Laura Giulia Pedrocchi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alessandra Laura Giulia Pedrocchi","raw_affiliation_strings":["Politecnico di Milano,Department of Electronics, Information and Bioengineering,Milan,Italy"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ming Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Shen","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Copenhagen,Denmark"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yan Kyaw Tun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan Kyaw Tun","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Copenhagen,Denmark"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Arsela Prelaj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arsela Prelaj","raw_affiliation_strings":["Istituto Nazionale dei Tumori,Department of Medical Oncology,Milan,Italy"],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Sokol Kosta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sokol Kosta","raw_affiliation_strings":["Aalborg University,Department of Electronic Systems,Copenhagen,Denmark"],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87559324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2074","last_page":"2083"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.24789999425411224,"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":0.24789999425411224,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.058800000697374344,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.03920000046491623,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6370000243186951},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.41290000081062317},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.38760000467300415},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3495999872684479},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.3345000147819519},{"id":"https://openalex.org/keywords/survival-analysis","display_name":"Survival analysis","score":0.27570000290870667}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6370000243186951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5401999950408936},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.512499988079071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.461899995803833},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.41290000081062317},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2694000005722046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bigdata66926.2025.11401431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata66926.2025.11401431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/f89ef671-789d-40ed-981c-56ad81b23b22","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/f89ef671-789d-40ed-981c-56ad81b23b22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zanitti, M, Miskovic, V, Trov\u00f2, F, Pedrocchi, A L G, Shen, M, Tun, Y K, Prelaj, A & Kosta, S 2025, A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities. in 2025 IEEE International Conference on Big Data (BigData)., 11401431, IEEE (Institute of Electrical and Electronics Engineers), IEEE International Conference on Big Data (BigData), pp. 2074-2083, 2025 IEEE International Conference on Big Data, BigData 2025, Macau, China, 08/12/2025. https://doi.org/10.1109/BigData66926.2025.11401431","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:re.public.polimi.it:11311/1309105","is_oa":false,"landing_page_url":"https://hdl.handle.net/11311/1309105","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/f89ef671-789d-40ed-981c-56ad81b23b22","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/f89ef671-789d-40ed-981c-56ad81b23b22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zanitti, M, Miskovic, V, Trov\u00f2, F, Pedrocchi, A L G, Shen, M, Tun, Y K, Prelaj, A & Kosta, S 2025, A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities. in 2025 IEEE International Conference on Big Data (BigData)., 11401431, IEEE (Institute of Electrical and Electronics Engineers), IEEE International Conference on Big Data (BigData), pp. 2074-2083, 2025 IEEE International Conference on Big Data, BigData 2025, Macau, China, 08/12/2025. https://doi.org/10.1109/BigData66926.2025.11401431","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.4296708405017853,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2017502278","https://openalex.org/W2169903102","https://openalex.org/W2549852019","https://openalex.org/W2795989238","https://openalex.org/W2797347913","https://openalex.org/W2936150952","https://openalex.org/W2949676527","https://openalex.org/W2963677766","https://openalex.org/W3028304854","https://openalex.org/W3094595351","https://openalex.org/W3147894994","https://openalex.org/W3168115304","https://openalex.org/W3176016422","https://openalex.org/W3176404283","https://openalex.org/W3203898052","https://openalex.org/W3205594709","https://openalex.org/W4206159997","https://openalex.org/W4288068240","https://openalex.org/W4290944617","https://openalex.org/W4293476620","https://openalex.org/W4304014045","https://openalex.org/W4319759388","https://openalex.org/W4362673398","https://openalex.org/W4392947521","https://openalex.org/W4393078503","https://openalex.org/W4394711534","https://openalex.org/W4398201291","https://openalex.org/W4400956650","https://openalex.org/W4402716294","https://openalex.org/W4406117717","https://openalex.org/W4406995550","https://openalex.org/W4409488923"],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"survival":[1,134,191],"outcomes":[2],"for":[3,55],"non-small":[4],"cell":[5],"lung":[6],"cancer":[7],"(NSCLC)":[8],"patients":[9],"is":[10,118,225],"challenging":[11],"due":[12],"to":[13,67,75,96,120,139,164,168,196,201,229],"the":[14,24,39,104,122,154,166,175,183,230],"different":[15],"individual":[16],"prognostic":[17],"features.":[18],"This":[19],"task":[20],"can":[21],"benefit":[22],"from":[23,124],"integration":[25,212,224],"of":[26,38,59,85,185,220],"whole-slide":[27],"images,":[28],"bulk":[29],"transcriptomics,":[30],"and":[31,110,136,178,193],"DNA":[32],"methylation,":[33],"which":[34],"offer":[35],"complementary":[36],"views":[37],"patient's":[40],"condition":[41],"at":[42],"diagnosis.":[43],"However,":[44],"real-world":[45],"clinical":[46],"datasets":[47,181],"are":[48],"often":[49],"incomplete,":[50],"with":[51,114,144],"entire":[52],"modalities":[53],"missing":[54,77],"a":[56,90,111,129,145],"significant":[57],"fraction":[58],"patients.":[60],"State-of-the-art":[61],"models":[62,74],"rely":[63],"on":[64,174,210,217],"available":[65],"data":[66,108],"create":[68],"patient-level":[69],"representations":[70],"or":[71],"use":[72],"generative":[73],"infer":[76],"modalities,":[78,221],"but":[79],"they":[80],"lack":[81],"robustness":[82,167,195],"in":[83,106,153,188],"cases":[84],"severe":[86,197],"missingness.":[87],"We":[88,127],"propose":[89,128],"Multimodal":[91],"Contrastive":[92],"Variational":[93],"AutoEncoder":[94],"(MCVAE)":[95],"address":[97],"this":[98],"issue:":[99],"modality-specific":[100],"variational":[101],"encoders":[102],"capture":[103],"uncertainty":[105],"each":[107],"source,":[109],"fusion":[112],"bottleneck":[113],"learned":[115],"gating":[116],"mechanisms":[117],"introduced":[119],"normalize":[121],"contributions":[123],"present":[125],"modalities.":[126],"multi-task":[130],"objective":[131],"that":[132,149,223],"combines":[133],"loss":[135,138,148],"reconstruction":[137],"regularize":[140],"patient":[141],"representations,":[142],"along":[143],"cross-modal":[146,151],"contrastive":[147],"enforces":[150],"alignment":[152],"latent":[155],"space.":[156],"During":[157],"training,":[158],"we":[159,206],"apply":[160],"stochastic":[161],"modality":[162],"masking":[163],"improve":[165],"arbitrary":[169],"missingness":[170,198],"patterns.":[171],"Extensive":[172],"evaluations":[173],"TCGA-LUAD":[176],"($n=475$)":[177],"TCGA-LUSC":[179],"($n=446$)":[180],"demonstrate":[182],"efficacy":[184],"our":[186,215],"approach":[187],"predicting":[189],"disease-specific":[190],"(DSS)":[192],"its":[194],"scenarios":[199],"compared":[200],"two":[202],"state-of-the-art":[203],"models.":[204],"Finally,":[205],"bring":[207],"some":[208],"clarifications":[209],"multimodal":[211],"by":[213],"testing":[214],"model":[216],"all":[218],"subsets":[219],"finding":[222],"not":[226],"always":[227],"beneficial":[228],"task.":[231]},"counts_by_year":[],"updated_date":"2026-03-21T08:13:44.787528","created_date":"2026-02-24T00:00:00"}
