MURA is a dataset of musculoskeletal radiographs consisting of 14, studies from 12, patients, with a total of 40, multi-view radiographic images.
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Each belongs to one of seven standard upper extremity radiographic study types: 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 and Test Set Collection To evaluate models and get a robust google dating groups x ray of radiologist performance, we collected additional labels from six board-certified Stanford radiologists on the test set, consisting of 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.
We randomly chose 3 of these radiologists to create https://dating7ek.info/14menu/nervous-about-dating-an-older-man-2446.php gold standard, defined as the majority vote of labels of the radiologists.
Our baseline uses a layer convolutional neural network to detect and localize abnormalities. The model takes as input one or more views for a study of an upper extremity.
On each view, our layer convolutional neural network predicts the probability of abnormality. We compute the overall probability of abnormality for the study by taking the arithmetic mean of the abnormality probabilities output by the network for each image.
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The model makes the binary prediction of abnormal if the probability of google dating groups x ray for the study is greater than 0. The network uses a Dense Convolutional Network architecture, which connects each layer to every other layer in a feed-forward fashion to make the optimization of deep networks tractable.
We replace the final fully connected layer with one that has a single output, after which we apply a sigmoid nonlinearity. We use Class Activation Maps to visualize the parts of the radiograph which contribute most to the model's prediction of abnormality.
How does our baseline do? Baseline performance is comparable to radiologist performance in detecting abnormalities on finger studies and equivalent on wrist studies.
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However, baseline performance is lower than best radiologist performance in detecting abnormalities on elbow, forearm, hand, humerus, shoulder studies, and overall, indicating that the task is a good challenge for future research. Downloading the Dataset v1. Once you register to download the MURA dataset, you will receive a link to the download over email.
Note that you may not share the link to download the dataset with others. Permission is granted to view and use the MURA Dataset without charge for personal, non-commercial research purposes only.
Any commercial use, sale, or other monetization is prohibited. If another user within your organization wishes to use the MURA Dataset, they must register as an individual user and comply with all the terms of this Research Use Agreement. In no event shall data or images generated through the use of the MURA Dataset be used or relied upon in the diagnosis or provision of patient care.
You will not make any attempt to re-identify any of the individual data subjects.
Re-identification of individuals is strictly prohibited. Any re-identification of any individual data subject shall be immediately reported to the School of Medicine. In the event that the School of Medicine determines that the recipient has violated this Research Use Agreement or other impermissible use has been made, the School of Medicine may direct that the undersigned data recipient immediately return all copies of the MURA Dataset and retain no copies thereof even if you did not cause the violation or impermissible use.
In consideration for your agreement to the terms and conditions contained here, Stanford grants you permission to view and use the MURA Dataset for personal, non-commercial research. You may not otherwise copy, reproduce, retransmit, distribute, publish, commercially exploit or otherwise transfer any material.
You agree to indemnify and hold Stanford harmless from any claims, losses or damages, including legal fees, arising out of or resulting from your use of the MURA Dataset or your violation or role in violation of these Terms. These Terms shall be governed by and interpreted in accordance with the laws.