• Description:

CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets. It consists of 224,316 chest radiographs of 65,240 patients, where the chest radiographic examinations and the associated radiology reports were retrospectively collected from Stanford Hospital. Each report was labeled for the presence of 14 observations as positive, negative, or uncertain. We decided on the 14 observations based on the prevalence in the reports and clinical relevance.

The CheXpert dataset must be downloaded separately after reading and agreeing to a Research Use Agreement. To do so, please follow the instructions on the website,

Split Examples
  • Feature structure:
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'image_view': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'label': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=4)),
    'name': Text(shape=(), dtype=string),
  • Feature documentation:
Feature Class Shape Dtype Description
image Image (None, None, 3) uint8
image_view ClassLabel int64
label Sequence(ClassLabel) (None,) int64
name Text string
  author    = {Jeremy Irvin and Pranav Rajpurkar and Michael Ko and Yifan Yu and Silviana Ciurea{-}Ilcus and Chris Chute and Henrik Marklund and Behzad Haghgoo and Robyn L. Ball and Katie Shpanskaya and Jayne Seekins and David A. Mong and Safwan S. Halabi and Jesse K. Sandberg and Ricky Jones and David B. Larson and Curtis P. Langlotz and Bhavik N. Patel and Matthew P. Lungren and Andrew Y. Ng},
  title     = {CheXpert: {A} Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison},
  journal   = {CoRR},
  volume    = {abs/1901.07031},
  year      = {2019},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1901.07031},
  timestamp = {Fri, 01 Feb 2019 13:39:59 +0100},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}