The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports. The dataset accompanies the publication of the MRNet work here.
The most common indications for the knee MRI examinations in this study included acute and chronic pain, follow-up or preoperative evaluation, injury/trauma. Examinations were performed with GE scanners (GE Discovery, GE Healthcare, Waukesha, WI) with standard knee MRI coil and a routine non-contrast knee MRI protocol that included the following sequences: coronal T1 weighted, coronal T2 with fat saturation, sagittal proton density (PD) weighted, sagittal T2 with fat saturation, and axial PD weighted with fat saturation. A total of 775 (56.6%) examinations used a 3.0-T magnetic field; the remaining used a 1.5-T magnetic field. See our paper for more details.
The exams have been split into a training set (1,130 exams, 1,088 patients), a validation set (called tuning set in the paper) (120 exams, 111 patients), and a hidden test set (called validation set in the paper) (120 exams, 113 patients). To form the validation and tuning sets, stratified random sampling was used to ensure that at least 50 positive examples of each label (abnormal, ACL tear, and meniscal tear) were present in each set. All exams from each patient were put in the same split.
The leaderboard reports the average AUC of the abnormality detection, ACL tear, and Meniscal tear tasks.
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We are hosting a competition to encourage others to develop models for automated interpretation of knee MRs. Our test set (called internal validation set in the paper) has its ground truth set using the majority vote of 3 practicing board-certified MSK radiologists (years in practice 6–19 years, average 12 years). The MSK radiologists had access to all DICOM series, the original report and clinical history, and follow-up exams during interpretation.
MRNet uses a hidden test set for official evaluation of models. Teams submit their executable code on Codalab, which is then run on a test set that is not publicly readable. Such a setup preserves the integrity of the test results.
Here's a tutorial walking you through official evaluation of your model. Once your model has been evaluated officially, your scores will be added to the leaderboard.
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