  We introduce MURA, a large dataset of musculoskeletal radiographs containing
40,561 images from 14,863 studies, where each study is manually labeled by
radiologists as either normal or abnormal. To evaluate models robustly and to
get an estimate of radiologist performance, we collect additional labels from
six board-certified Stanford radiologists on the test set, consisting of 207
musculoskeletal studies. On this test set, the majority vote of a group of
three radiologists serves as gold standard. We train a 169-layer DenseNet
baseline model to detect and localize abnormalities. Our model achieves an
AUROC of 0.929, with an operating point of 0.815 sensitivity and 0.887
specificity. We compare our model and radiologists on the Cohen's kappa
statistic, which expresses the agreement of our model and of each radiologist
with the gold standard. Model performance is comparable to the best radiologist
performance in detecting abnormalities on finger and wrist studies. However,
model performance is lower than best radiologist performance in detecting
abnormalities on elbow, forearm, hand, humerus, and shoulder studies. We
believe that the task is a good challenge for future research. To encourage
advances, we have made our dataset freely available at
https://stanfordmlgroup.github.io/competitions/mura .
