# Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation Source: [Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:46:07.580Z Authors: Saahil Jain, Akshay Smit, Andrew Y. Ng, Pranav Rajpurkar Published: 2021-04-01T22:37:29.000Z ## Abstract Although deep learning models for chest X-ray interpretation are commonly trained on labels generated by automatic radiology report labelers, the impact of improvements in report labeling on the performance of chest X-ray classification models has not been systematically investigated. We first compare the CheXpert, CheXbert, and VisualCheXbert labelers on the task of extracting accurate chest X-ray image labels from radiology reports, reporting that the VisualCheXbert labeler outperforms the CheXpert and CheXbert labelers. Next, after training image classification models using labels generated from the different radiology report labelers on one of the largest datasets of chest X-rays, we show that an image classification model trained on labels from the VisualCheXbert labeler outperforms image classification models trained on labels from the CheXpert and CheXbert labelers. Our work suggests that recent improvements in radiology report labeling can translate to the development of higher performing chest X-ray classification models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()