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StanfordAIMI/GREEN

88 downloads in 30 days5 likesLast modified 5 Dec 2024View on Hugging Face

Permissive or share-alike

The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 72febb4
Licence files: No licence file at the top level of the repositoryEvidence: file list at 72febb4
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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text-generation

Citation text from the card

@inproceedings{ostmeier-etal-2024-green,
    title = "{GREEN}: Generative Radiology Report Evaluation and Error Notation",
    author = "Ostmeier, Sophie  and
      Xu, Justin  and
      Chen, Zhihong  and
      Varma, Maya  and
      Blankemeier, Louis  and
      Bluethgen, Christian  and
      Md, Arne Edward Michalson  and
      Moseley, Michael  and
      Langlotz, Curtis  and
      Chaudhari, Akshay S  and
      Delbrouck, Jean-Benoit",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.21",
    doi = "10.18653/v1/2024.findings-emnlp.21",
    pages = "374--390",
    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

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dataset_licence({ "id": "StanfordAIMI/GREEN" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0