{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ratescore-a-metric-for-radiology-report","title":"RaTEScore: A Metric for Radiology Report Generation","arxiv_id":"2406.16845","date":"2024-06-24","proceeding":null,"authors":["Weike Zhao","Chaoyi Wu","Xiaoman Zhang","Ya zhang","Yanfeng Wang","Weidi Xie"],"abstract":"This paper introduces a novel, entity-aware metric, termed as Radiological Report (Text) Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models. RaTEScore emphasizes crucial medical entities such as diagnostic outcomes and anatomical details, and is robust against complex medical synonyms and sensitive to negation expressions. Technically, we developed a comprehensive medical NER dataset, RaTE-NER, and trained an NER model specifically for this purpose. This model enables the decomposition of complex radiological reports into constituent medical entities. The metric itself is derived by comparing the similarity of entity embeddings, obtained from a language model, based on their types and relevance to clinical significance. Our evaluations demonstrate that RaTEScore aligns more closely with human preference than existing metrics, validated both on established public benchmarks and our newly proposed RaTE-Eval benchmark.","url_abs":"https://arxiv.org/abs/2406.16845v2","url_pdf":"https://arxiv.org/pdf/2406.16845v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ratescore-a-metric-for-radiology-report","repo_url":"https://github.com/MAGIC-AI4Med/RaTEScore","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ratescore-a-metric-for-radiology-report","repo_url":"https://github.com/chaoyi-wu/pmc-llama","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"ratescore-a-metric-for-radiology-report","repo_url":"https://github.com/ljy19970415/unibrain","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"entity-embeddings","task_name":"Entity Embeddings"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"negation","task_name":"Negation"}],"methods":[],"datasets_introduced":[{"slug":"rate-eval","name":"RaTE-Eval","full_name":""},{"slug":"rate-ner","name":"RaTE-NER","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.16845","atlas_url":"https://app.syntology.ai/?focus=2406.16845","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}