Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, Matthew Lungren
Abstract
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 BERT-based 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 a biomedically pretrained BERT model first trained on annotations of a rule-based labeler and then finetuned on a small set of expert annotations augmented with automated backtranslation. We find that our final model, CheXbert, is able to outperform the previous best rules-based labeler with statistical significance, setting a new SOTA for report labeling on one of the largest datasets of chest x-rays.- Anthology ID:
- 2020.emnlp-main.117
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1500–1519
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.117
- DOI:
- 10.18653/v1/2020.emnlp-main.117
- Cite (ACL):
- Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, and Matthew Lungren. 2020. Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1500–1519, Online. Association for Computational Linguistics.
- Cite (Informal):
- Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (Smit et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.117.pdf