MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs
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We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,895 images from 14,982 studies, where each study is manually labeled by radiologists as either normal or abnormal. On this dataset, we train a 169-layer densely connected convolutional network to detect and localize abnormalities. To evaluate our model robustly and to get an estimate of radiologist performance, we collect additional labels from board-certified Stanford radiologists on the test set, consisting of 209 musculoskeletal studies. We compared 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, defined as the majority vote of a disjoint group of radiologists. We find that our model achieves performance comparable to that of radiologists. Model performance is higher than the best radiologist performance in detecting abnormalities on finger studies and equivalent on wrist studies. However, model performance is lower than best radiologist performance in detecting abnormalities on elbow, forearm, hand, humerus, and shoulder studies, indicating 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/projects/mura
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