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The RaTE-NER dataset is a large-scale radiology named entity recognition (NER) dataset, containing 13,235 manually annotated sentences from 1,816 reports in the MIMIC-IV database, covering nine imaging modalities and 23 anatomical regions to ensure comprehensive coverage. Additionally, by leveraging GPT-4 and other medical knowledge bases, the dataset further enriches 33,605 sentences from 17,432 reports in Radiopaedia, capturing the complexity and subtleties of rare diseases and abnormalities. The dataset provides two preprocessing formats to support different NER approaches and clearly outlines the file paths and structure.
