用大模型自动提取脊柱MRI报告标签，效果媲美GPT-4。
Automated Spinal MRI Labelling from Reports Using a Large Language Model
- 基于大语言模型构建自动化标签提取流程
- 在五类脊柱疾病上表现达GPT-4水平
- 自动生成标签可训练出媲美人工标注的影像分类器
我们提出一种通用流程，利用大语言模型自动化提取放射科报告中的标签，并在脊柱MRI报告上进行验证。方法在五种不同病症（脊柱癌、狭窄、滑脱、马尾神经压迫、椎间盘突出）上评估有效性。使用开源模型，该方法在独立测试集上的表现等于或超过GPT-4。此外，我们证明所提取的标签可用于训练影像分类模型，对相应MR扫描中的病变进行识别。使用自动标签训练的分类器性能与使用临床医生人工标注的数据训练的模型相当。代码已公开于 https://github.com/robinyjpark/AutoLabelClassifier。
原文摘要 · Abstract (English)
We propose a general pipeline to automate the extraction of labels from radiology reports using large language models, which we validate on spinal MRI reports. The efficacy of our labelling method is measured on five distinct conditions: spinal cancer, stenosis, spondylolisthesis, cauda equina compression and herniation. Using open-source models, our method equals or surpasses GPT-4 on a held-out set of reports. Furthermore, we show that the extracted labels can be used to train imaging models to classify the identified conditions in the accompanying MR scans. All classifiers trained using automated labels achieve comparable performance to models trained using scans manually annotated by clinicians. Code can be found at https://github.com/robinyjpark/AutoLabelClassifier.
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