by Pranav Rajpurkar,Jeremy Irvin,Aarti Bagul,Daisy Ding,Tony Duan,Hershel Mehta,Brandon Yang,Kaylie Zhu,Dillon Laird,Robyn L. Ball,Curtis Langlotz,Katie Shpanskaya,Matthew P. Lungren,Andrew Y. NgUnknown
by Pranav Rajpurkar,Jeremy Irvin,Aarti Bagul,Daisy Ding,Tony Duan,Hershel Mehta,Brandon Yang,Kaylie Zhu,Dillon Laird,Robyn L. Ball,Curtis Langlotz,Katie Shpanskaya,Matthew P. Lungren,Andrew Y. NgLicense : Unknown
What is MURA MURA (musculoskeletal radiographs) is a large dataset of bone X-rays. Algorithms are tasked with determining whether an X-ray study is normal or abnormal. Musculoskeletal conditions affect more than 1.7 billion people worldwide, and are the most common cause of severe, long-term pain and disability, with 30 million emergency department visits annually and increasing. We hope that our dataset can lead to significant advances in medical imaging technologies which can diagnose at the level of experts, towards improving healthcare access in parts of the world where access to skilled radiologists is limited. MURA is one of the largest public radiographic image datasets. We're making this dataset available to the community and hosting a competition to see if your models can perform as well as radiologists on the task. How did we collect MURA MURA is a dataset of musculoskeletal radiographs consisting of 14,863 studies from 12,173 patients, with a total of 40,561 multi-view radiographic images. Each belongs to one of seven standard upper extremity radiographic study types: elbow, finger, forearm, hand, humerus, shoulder, and wrist. Each study was manually labeled as normal or abnormal by board-certified radiologists from the Stanford Hospital at the time of clinical radiographic interpretation in the diagnostic radiology environment between 2001 and 2012. Test Set Collection To evaluate models and get a robust estimate of radiologist performance, we collected additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies. The radiologists individually retrospectively reviewed and labeled each study in the test set as a DICOM file as normal or abnormal in the clinical reading room environment using the PACS system. The radiologists have 8.83 years of experience on average ranging from 2 to 25 years. We randomly chose 3 of these radiologists to create a gold standard, defined as the majority vote of labels of the radiologists.