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Large, labeled datasets have driven deep learning methods to achieveexpert-level performance on a variety of medical imaging tasks. We presentCheXpert, a large dataset that contains 224,316 chest radiographs of 65,240patients. We design a labeler to automatically detect the presence of 14observations in radiology reports, capturing uncertainties inherent inradiograph interpretation. We investigate different approaches to using theuncertainty labels for training convolutional neural networks that output theprobability of these observations given the available frontal and lateralradiographs. On a validation set of 200 chest radiographic studies which weremanually annotated by 3 board-certified radiologists, we find that differentuncertainty approaches are useful for different pathologies. We then evaluateour best model on a test set composed of 500 chest radiographic studiesannotated by a consensus of 5 board-certified radiologists, and compare theperformance of our model to that of 3 additional radiologists in the detectionof 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, themodel ROC and PR curves lie above all 3 radiologist operating points. Werelease the dataset to the public as a standard benchmark to evaluateperformance of chest radiograph interpretation models.  The dataset is freely available athttps://stanfordmlgroup.github.io/competitions/chexpert .

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