{"id":"https://openalex.org/W4200270480","doi":"https://doi.org/10.1109/embc46164.2021.9630698","title":"Training with Small Medical Data: Robust Bayesian Neural Networks for Colon Cancer Overall Survival Prediction","display_name":"Training with Small Medical Data: Robust Bayesian Neural Networks for Colon Cancer Overall Survival Prediction","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200270480","doi":"https://doi.org/10.1109/embc46164.2021.9630698","pmid":"https://pubmed.ncbi.nlm.nih.gov/34891686"},"language":"en","primary_location":{"id":"doi:10.1109/embc46164.2021.9630698","is_oa":true,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9630698","pdf_url":"https://ieeexplore.ieee.org/ielx7/9629355/9629471/09630698.pdf","source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/9629355/9629471/09630698.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Te-Cheng Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Te-Cheng Hsu","raw_affiliation_strings":["Institute of Communication Engineering, National Tsing-Hua University (NTHU), Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Communication Engineering, National Tsing-Hua University (NTHU), Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"last","author":{"id":null,"display_name":"Che Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Che Lin","raw_affiliation_strings":["Department of Electrical Engineering, Graduate Institute of Communication Engineering, National Taiwan University (NTU), Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Graduate Institute of Communication Engineering, National Taiwan University (NTU), Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I25846049"],"apc_list":null,"apc_paid":null,"fwci":0.635,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.713903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2021","issue":null,"first_page":"2030","last_page":"2033"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.27230000495910645,"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":0.27230000495910645,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.1437000036239624,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.11599999666213989,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.5608999729156494},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5375999808311462},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49939998984336853},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.46480000019073486},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.43220001459121704},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40369999408721924},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.36559998989105225},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3610999882221222},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3580000102519989}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6736999750137329},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5724999904632568},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5669000148773193},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5608999729156494},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5375999808311462},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.46480000019073486},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3605000078678131},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.29820001125335693},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.25839999318122864}],"mesh":[{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003110","descriptor_name":"Colonic Neoplasms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003110","descriptor_name":"Colonic Neoplasms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003110","descriptor_name":"Colonic Neoplasms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012372","descriptor_name":"ROC Curve","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012372","descriptor_name":"ROC Curve","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012372","descriptor_name":"ROC Curve","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D049490","descriptor_name":"Systems Biology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D049490","descriptor_name":"Systems Biology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D049490","descriptor_name":"Systems Biology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc46164.2021.9630698","is_oa":true,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9630698","pdf_url":"https://ieeexplore.ieee.org/ielx7/9629355/9629471/09630698.pdf","source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:34891686","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34891686","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":{"id":"doi:10.1109/embc46164.2021.9630698","is_oa":true,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9630698","pdf_url":"https://ieeexplore.ieee.org/ielx7/9629355/9629471/09630698.pdf","source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200270480.pdf","grobid_xml":"https://content.openalex.org/works/W4200270480.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W2005422315","https://openalex.org/W2028663226","https://openalex.org/W2038252730","https://openalex.org/W2115108310","https://openalex.org/W2150780222","https://openalex.org/W2163485494","https://openalex.org/W2911188335","https://openalex.org/W2969567836","https://openalex.org/W3010657985","https://openalex.org/W3011821385","https://openalex.org/W6617145748","https://openalex.org/W6638667902","https://openalex.org/W6638836233","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6677533672","https://openalex.org/W6744493999","https://openalex.org/W6757107679","https://openalex.org/W6769077659"],"related_works":[],"abstract_inverted_index":{"Fast":[0],"and":[1,51,74,112,137,195],"accurate":[2],"cancer":[3,99],"prognosis":[4],"stratification":[5],"models":[6,21,40,108,216],"are":[7,32,41],"essential":[8,212],"for":[9],"treatment":[10],"designs.":[11],"Large":[12],"labeled":[13,29],"patient":[14,30,180],"data":[15,31,49,181],"can":[16],"power":[17],"advanced":[18],"deep":[19,39],"learning":[20,215],"to":[22,34,43,71,90,117,213],"obtain":[23],"precise":[24],"predictions.":[25],"However,":[26],"since":[27],"fully":[28],"hard":[33],"acquire":[35],"in":[36,67,101,122,191],"practical":[37],"scenarios,":[38],"prone":[42],"make":[44],"non-robust":[45],"predictions":[46,115],"biased":[47],"toward":[48],"partition":[50],"model":[52],"hyper-parameter":[53],"selection.":[54],"Given":[55],"a":[56],"small":[57,218],"training":[58,214],"set,":[59],"we":[60,85,141],"applied":[61],"the":[62,92,125,128,144,154],"systems":[63],"biology":[64],"feature":[65],"selector":[66],"our":[68],"previous":[69],"study":[70],"avoid":[72],"over-fitting":[73],"select":[75],"18":[76],"prognostic":[77],"biomarkers.":[78],"Combined":[79],"with":[80,178,217],"three":[81],"other":[82],"clinical":[83],"features,":[84],"trained":[86],"Bayesian":[87,107,145,172],"binary":[88],"classifiers":[89],"predict":[91],"5-year":[93],"overall":[94],"survival":[95],"(OS)":[96],"of":[97,124,193],"colon":[98],"patients":[100],"this":[102],"study.":[103],"Results":[104],"showed":[105],"that":[106,143],"could":[109],"provide":[110],"better":[111],"more":[113,188],"robust":[114,189],"compared":[116],"their":[118],"non-Bayesian":[119],"counterparts.":[120],"Specifically,":[121],"terms":[123,192],"area":[126],"under":[127],"receiver":[129],"operating":[130],"characteristic":[131],"curve":[132],"(AUC),":[133],"macro":[134],"F1-score":[135],"(maF<sub>1</sub>),":[136],"concordance":[138],"index":[139],"(CI),":[140],"found":[142],"bimodal":[146],"neural":[147,173],"network":[148,174],"(late":[149],"fusion)":[150,183],"classifier":[151,175],"(B-Bimodal)":[152],"achieved":[153,184],"best":[155],"results":[156],"(AUC:":[157,197],"0.8083":[158],"\u00b1":[159,163,167,199,203,207],"0.0736;":[160],"maF<sub>1</sub>:":[161,201],"0.7300":[162],"0.0659;":[164],"CI:":[165,205],"0.7238":[166],"0.0440).":[168],"The":[169],"single":[170],"modal":[171],"(B-Concat)":[176],"fed":[177],"concatenated":[179],"(early":[182],"slightly":[185],"worse":[186],"but":[187],"performance":[190],"AUC":[194],"CI":[196],"0.7105":[198],"0.0692;":[200],"0.7156":[202],"0.0690;":[204],"0.6627":[206],"0.0558).":[208],"Such":[209],"robustness":[210],"is":[211],"medical":[219],"data.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2021-12-31T00:00:00"}
