CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Understand

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks.

• We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients.
• We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation.
• We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs.