Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.

本文介绍了一种基于Transformer网络的医学影像分类方法，通过自动标注专家医学报告中的图像标签，克服了大规模数据集标注的困难，其表现相当于专家放射科医师，高于专业医生，该方法可行性很高，并提供了在线代码以帮助研究人员标记自己的MRI数据集以供医学应用。

使用注意力模型自动标注 MRI 扫描放射学报告（ALARM）