用大模型自动把病历中的解剖标志定位到影像上
Automatic Mapping of Anatomical Landmarks from Free-Text Using Large Language Models: Insights from Llama-2
- 用Llama-2分析病历文本，线性映射解剖标志空间位置
- 对不同提示词保持稳定，定位准确率高
- 适合医学影像自动化标注与临床辅助系统
解剖标志在医学影像导航和异常检测中至关重要。现代大型语言模型（如Llama-2）为自动化提取自由文本放射科报告中的解剖标志并将其映射到图像数据对应位置提供了可能。近期研究指出，大模型可能具备生成过程的连贯表征。受此启发，我们探究了大模型是否能准确表示解剖标志的空间位置。通过使用Llama-2模型的实验发现，它们能以高度鲁棒性的方式在线性空间中表示解剖标志。这些结果凸显了大模型在提升医学影像工作流程效率与准确性方面的潜力。
原文摘要 · Abstract (English)
Anatomical landmarks are vital in medical imaging for navigation and anomaly detection. Modern large language models (LLMs), like Llama-2, offer promise for automating the mapping of these landmarks in free-text radiology reports to corresponding positions in image data. Recent studies propose LLMs may develop coherent representations of generative processes. Motivated by these insights, we investigated whether LLMs accurately represent the spatial positions of anatomical landmarks. Through experiments with Llama-2 models, we found that they can linearly represent anatomical landmarks in space with considerable robustness to different prompts. These results underscore the potential of LLMs to enhance the efficiency and accuracy of medical imaging workflows.
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