  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.
