{
 "id": "StanfordAIMI/GREEN",
 "owner": "StanfordAIMI",
 "name": "GREEN",
 "downloads": 88,
 "likes": 5,
 "last_modified": "2024-12-05T23:31:53.000Z",
 "stated_licence": "apache-2.0",
 "stated_from": "tag",
 "licence_name": null,
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 "gated": false,
 "tasks": [
  "text-generation"
 ],
 "citation": "@inproceedings{ostmeier-etal-2024-green,\n    title = \"{GREEN}: Generative Radiology Report Evaluation and Error Notation\",\n    author = \"Ostmeier, Sophie  and\n      Xu, Justin  and\n      Chen, Zhihong  and\n      Varma, Maya  and\n      Blankemeier, Louis  and\n      Bluethgen, Christian  and\n      Md, Arne Edward Michalson  and\n      Moseley, Michael  and\n      Langlotz, Curtis  and\n      Chaudhari, Akshay S  and\n      Delbrouck, Jean-Benoit\",\n    editor = \"Al-Onaizan, Yaser  and\n      Bansal, Mohit  and\n      Chen, Yun-Nung\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2024\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, Florida, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.findings-emnlp.21\",\n    doi = \"10.18653/v1/2024.findings-emnlp.21\",\n    pages = \"374--390\",\n    abstract = \"Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to its medical nature. Existing automatic evaluation metrics either suffer from failing to consider factual correctness (e.g., BLEU and ROUGE) or are limited in their interpretability (e.g., F1CheXpert and F1RadGraph). In this paper, we introduce GREEN (Generative Radiology Report Evaluation and Error Notation), a radiology report generation metric that leverages the natural language understanding of language models to identify and explain clinically significant errors in candidate report",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive.",
 "revision": "72febb4068aa29ae4303ebad7483d5b71cca9a31",
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}