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

AMIA ... Annual Symposium proceedings, 2021, Vol.2021, p.668-676 [Peer Reviewed Journal]

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Title:
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation
Author: Lau, Wilson ; Aaltonen, Laura ; Gunn, Martin ; Yetisgen, Meliha
Subjects: Humans ; Language ; Natural Language Processing ; Radiography ; Radiology ; Support Vector Machine
Is Part Of: AMIA ... Annual Symposium proceedings, 2021, Vol.2021, p.668-676
Description: 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 computed tomography examinations, by pre-training a domain-specific BERT model (BERT ). 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 n-gram models using Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Random Forest (RF) classifiers, as well as the BERT model. SVM, GBM and RF achieved macro-averaged F1 scores of 0.45, 0.45, and 0.6 while BERT and BERT achieved 0.61 and 0.63. Knowledge distillation boosted performance on the minority classes and achieved an F1 score of 0.66.
Publisher: United States: American Medical Informatics Association
Language: English
Identifier: EISSN: 1942-597X
PMID: 35308920
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central

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