Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation

Selecting radiology examination protocol is a repetitive, error-prone, and time-consuming process. In this paper, we present a deep learning approach to automatically assign protocols to computer tomography examinations, by pre-training a domain-specific BERT model ($BERT_{rad}$). To handle the high data imbalance across exam protocols, we used a knowledge distillation approach that up-sampled the minority classes through data augmentation. We compared classification performance of the described approach with the statistical n-gram models using Support Vector Machine (SVM) and Random Forest (RF) classifiers, as well as the Google's $BERT_{base}$ model. SVM and RF achieved macro-averaged F1 scores of 0.45 and 0.6 while $BERT_{base}$ and $BERT_{rad}$ achieved 0.61 and 0.63. Knowledge distillation improved overall performance on the minority classes, achieving a F1 score of 0.66. Additionally, by choosing the optimal threshold, the BERT models could classify over 50% of test samples within 5% error rate and potentially alleviate half of radiologist protocoling workload.