{"id":"https://openalex.org/W7144334197","doi":"https://doi.org/10.20736/0002002076","title":"Ubie at the NTCIR-18 RadNLP Main Task: Few-shot Classification of TNM Staging for Japanese Radiology Reports Using LLMs","display_name":"Ubie at the NTCIR-18 RadNLP Main Task: Few-shot Classification of TNM Staging for Japanese Radiology Reports Using LLMs","publication_year":null,"publication_date":null,"ids":{"openalex":"https://openalex.org/W7144334197","doi":"https://doi.org/10.20736/0002002076"},"language":"en","primary_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0006839239","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2002076","pdf_url":"https://repository.nii.ac.jp/record/2002076/files/16-NTCIR18-RADNLP-NishibayashiT.pdf","source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"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":"conference paper"},"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://repository.nii.ac.jp/record/2002076/files/16-NTCIR18-RADNLP-NishibayashiT.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5131282884","display_name":"Takashi Nishibayashi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140213","display_name":"United Biomedical (United States)","ror":"https://ror.org/044bp3769","country_code":"US","type":"company","lineage":["https://openalex.org/I4210140213"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Takashi Nishibayashi","raw_affiliation_strings":["Ubie, Inc . Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ubie, Inc . Japan","institution_ids":["https://openalex.org/I4210140213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131005239","display_name":"Mitsuhisa Ota","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140213","display_name":"United Biomedical (United States)","ror":"https://ror.org/044bp3769","country_code":"US","type":"company","lineage":["https://openalex.org/I4210140213"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mitsuhisa Ota","raw_affiliation_strings":["Ubie, Inc . Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ubie, Inc . Japan","institution_ids":["https://openalex.org/I4210140213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015670174","display_name":"Masahiro Kazama","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140213","display_name":"United Biomedical (United States)","ror":"https://ror.org/044bp3769","country_code":"US","type":"company","lineage":["https://openalex.org/I4210140213"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Masahiro Kazama","raw_affiliation_strings":["Ubie, Inc . Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ubie, Inc . Japan","institution_ids":["https://openalex.org/I4210140213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210140213"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"none","last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5270000100135803,"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/T10028","display_name":"Topic Modeling","score":0.5270000100135803,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.10740000009536743,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T11894","display_name":"Radiology practices and education","score":0.05119999870657921,"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/case-based-reasoning","display_name":"Case-based reasoning","score":0.3492000102996826},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3418999910354614},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.32359999418258667},{"id":"https://openalex.org/keywords/model-based-reasoning","display_name":"Model-based reasoning","score":0.30300000309944153},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.2799000144004822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5109000205993652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4009000062942505},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3718000054359436},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.32190001010894775},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.28029999136924744},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C59594135","wikidata":"https://www.wikidata.org/wiki/Q5249242","display_name":"Decision model","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27129998803138733}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:irdb.nii.ac.jp:03100:0006839239","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2002076","pdf_url":"https://repository.nii.ac.jp/record/2002076/files/16-NTCIR18-RADNLP-NishibayashiT.pdf","source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"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":"conference paper"}],"best_oa_location":{"id":"pmh:oai:irdb.nii.ac.jp:03100:0006839239","is_oa":true,"landing_page_url":"https://repository.nii.ac.jp/records/2002076","pdf_url":"https://repository.nii.ac.jp/record/2002076/files/16-NTCIR18-RADNLP-NishibayashiT.pdf","source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"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":"conference paper"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.524472177028656}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7144334197.pdf","grobid_xml":"https://content.openalex.org/works/W7144334197.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"Ubie":[1],"team":[2],"participated":[3],"in":[4,85,161],"the":[5,27,31,111,117,200],"RadNLP":[6],"core":[7],"task":[8],"on":[9,15,36,98,154,188],"lung":[10],"cancer":[11],"staging":[12],"classification":[13,38,89,165],"based":[14,97],"Japanese":[16],"radiology":[17],"reports":[18,22],"at":[19],".This":[20],"paper":[21],"our":[23,162],"approach":[24],"and":[25,58,72,126,185],"analyzes":[26],"official":[28],"results.We":[29],"investigated":[30],"impact":[32],"of":[33,50,91,168],"prompt":[34,63,92,194],"engineering":[35,195],"TNM":[37],"using":[39,61],"large":[40],"language":[41],"models":[42,47,102,141,217],"(LLMs).We":[43],"compared":[44],"multiple":[45],"proprietary":[46],"available":[48],"as":[49],"January":[51],"2025":[52],"(Gemini":[53],"1.5":[54,177],"Pro,":[55,178],"Gemini":[56,101,176],"Exp.1206,":[57],"OpenAI":[59],"o1)":[60],"various":[62],"configurations,":[64],"including":[65],"zero-shot,":[66],"few-shot,":[67],"chain-of-thought":[68],"(CoT),":[69],"self-feedbacked":[70,169],"instruction,":[71],"medical":[73,87,163],"ontology":[74],"enhancement.The":[75],"results":[76],"demonstrate":[77],"significant":[78],"performance":[79,114,207],"improvements":[80],"driven":[81],"by":[82],"model":[83,99,122,202],"evolution":[84],"this":[86],"text":[88,164],"task.Analysis":[90],"variations":[93],"revealed":[94],"differential":[95],"impacts":[96],"capabilities.For":[100],"tested,":[103],"explicitly":[104],"prompting":[105,155],"reasoning":[106,121,133,147,156,201,221],"steps":[107],"(CoT)":[108],"led":[109],"to":[110,181],"most":[112],"substantial":[113,206],"gains.In":[115],"contrast,":[116],"o1":[118],"model,":[119],"a":[120],"performing":[123],"internal":[124],"CoT":[125],"self-evaluation,":[127],"showed":[128],"limited":[129,197],"benefit":[130],"from":[131],"explicit":[132],"prompts,":[134],"suggesting":[135],"that":[136],"strategies":[137],"effective":[138],"for":[139,145,175,199,209,215],"non-reasoning":[140,210],"are":[142],"less":[143],"critical":[144],"advanced":[146,220],"models.This":[148],"finding,":[149],"consistent":[150],"with":[151],"general":[152],"guidance":[153],"models,":[157,211],"is":[158],"also":[159],"observed":[160],"experiments.The":[166],"effectiveness":[167],"instruction":[170],"varied,":[171],"showing":[172],"no":[173],"improvement":[174],"possibly":[179],"due":[180],"inadequate":[182],"feedback":[183],"generation":[184],"its":[186,213],"dependence":[187],"factors":[189],"like":[190],"few-shot":[191],"example":[192],"selection.While":[193],"offered":[196],"gains":[198],"evaluated,":[203],"it":[204],"provided":[205],"benefits":[208],"highlighting":[212],"value":[214],"optimizing":[216],"without":[218],"inherent":[219],"capabilities.":[222]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-04-01T00:00:00"}
