Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
This addresses the bottleneck of manual labeling for medical imaging researchers, though it is incremental as it applies an existing transformer method to a specific domain.
The paper tackles the problem of labeling large MRI datasets for deep learning by introducing a transformer-based network that automatically assigns image labels from free-text radiology reports. The model's performance is comparable to an expert radiologist and better than an expert physician, demonstrating feasibility for medical imaging applications.
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.