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 .
Selecting radiology examination protocol is a repetitive, 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 .
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error-prone,
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. 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