CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

TL;DR

CheXbert is a novel BERT-based model that combines rule-based system outputs and expert annotations to improve the accuracy of radiology report labeling, surpassing previous methods on large chest X-ray datasets.

Contribution

Introduces CheXbert, a hybrid BERT model that leverages rule-based and expert annotations for superior radiology report labeling performance.

Findings

CheXbert outperforms previous rules-based labelers with statistical significance.

The model sets a new state-of-the-art for chest X-ray report labeling.

Combining rule-based and expert annotations enhances labeling accuracy.

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.