{"id":"https://openalex.org/W4412889900","doi":"https://doi.org/10.18653/v1/2025.acl-long.1279","title":"RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection","display_name":"RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412889900","doi":"https://doi.org/10.18653/v1/2025.acl-long.1279"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.1279","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1279","pdf_url":"https://aclanthology.org/2025.acl-long.1279.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.1279.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101917828","display_name":"Wenjun Hou","orcid":"https://orcid.org/0000-0001-9747-5894"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenjun Hou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102170397","display_name":"Cheng Yi","orcid":"https://orcid.org/0009-0003-1496-9779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050343206","display_name":"Kaishuai Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaishuai Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338822","display_name":"Heng Li","orcid":"https://orcid.org/0000-0003-4815-0537"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heng Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069459185","display_name":"Yan Hu","orcid":"https://orcid.org/0000-0002-4172-078X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100408997","display_name":"Wenjie Li","orcid":"https://orcid.org/0009-0000-4935-3382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenjie Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103198854","display_name":"Jiang Liu","orcid":"https://orcid.org/0000-0003-1700-2654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5175,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93078499,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"26366","last_page":"26381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9941999912261963,"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":0.9941999912261963,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9902999997138977,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9632999897003174,"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/computer-science","display_name":"Computer science","score":0.5314896106719971},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.4749974012374878},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.3884000778198242},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.20090025663375854},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12612175941467285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5314896106719971},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.4749974012374878},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.3884000778198242},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20090025663375854},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12612175941467285}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.1279","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1279","pdf_url":"https://aclanthology.org/2025.acl-long.1279.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.1279","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1279","pdf_url":"https://aclanthology.org/2025.acl-long.1279.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8999964948","display_name":null,"funder_award_id":"82272086","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412889900.pdf","grobid_xml":"https://content.openalex.org/works/W4412889900.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Large":[0],"language":[1,137],"models":[2],"(LLMs)":[3],"have":[4,16],"demonstrated":[5],"remarkable":[6],"capabilities":[7],"in":[8,135],"various":[9],"domains,":[10],"including":[11],"radiology":[12,61,119],"report":[13,62,69],"generation.Previous":[14],"approaches":[15,36],"attempted":[17],"to":[18,47,104],"utilize":[19],"multimodal":[20],"LLMs":[21,134],"for":[22,59],"this":[23,52,107],"task,":[24],"enhancing":[25,60],"their":[26],"performance":[27],"through":[28],"the":[29,39,44,75,88],"integration":[30],"of":[31,78],"domainspecific":[32],"knowledge":[33,40,66,77,91,103],"retrieval.However,":[34],"these":[35],"often":[37],"overlook":[38],"already":[41],"embedded":[42],"within":[43],"LLMs,":[45],"leading":[46],"redundant":[48],"information":[49],"integration.To":[50],"address":[51],"limitation,":[53],"we":[54],"propose":[55],"RADAR,":[56],"a":[57],"framework":[58],"generation":[63,70],"with":[64,94],"supplementary":[65,102],"injection.RADAR":[67],"improves":[68],"by":[71,109],"systematically":[72],"leveraging":[73],"both":[74,111,136],"internal":[76],"an":[79],"LLM":[80],"and":[81,117,125,139],"externally":[82],"retrieved":[83],"information.Specifically,":[84],"it":[85],"first":[86],"extracts":[87],"model's":[89],"acquired":[90],"that":[92,129],"aligns":[93],"expert":[95],"imagebased":[96],"classification":[97],"outputs.It":[98],"then":[99],"retrieves":[100],"relevant":[101],"further":[105],"enrich":[106],"information.Finally,":[108],"aggregating":[110],"sources,":[112],"RADAR":[113],"generates":[114],"more":[115],"accurate":[116],"informative":[118],"reports.Extensive":[120],"experiments":[121],"on":[122],"MIMIC-CXR,":[123],"CHEXPERT-PLUS,":[124],"IU":[126],"X-RAY":[127],"demonstrate":[128],"our":[130],"model":[131],"outperforms":[132],"state-of-the-art":[133],"quality":[138],"clinical":[140],"accuracy":[141],"1":[142],".":[143]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
