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Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge or manual annotations by experts. In this work, we introduce a BERTbased approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations. We demonstrate superior performance of adoi:10.18653/v1/2020.emnlp-main.117 fatcat:kbdqwqwelfbyzldxb6xybh57jq