{"url":"/dataset/mrnet","name":"MRNet","full_name":null,"description_markdown":"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. \r\n\r\nSource: [MRNet](https://stanfordmlgroup.github.io/competitions/mrnet/)","description_withheld":null,"homepage":"https://stanfordmlgroup.github.io/competitions/mrnet/","introduced_date":"2018-11-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-learning-assisted-diagnosis-for-knee","title":"Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet","first_author":"Nicholas Bien","url":null},"license":null,"modalities":[],"tasks":[{"name":"Multi-Label Classification","url":"/task/multi-label-classification","datasets_with_task":"/datasets/task/multi-label-classification"},{"name":"Data Augmentation","url":"/task/data-augmentation","datasets_with_task":"/datasets/task/data-augmentation"}],"languages":[],"variants":["MRNet"],"data_loaders":[{"repo":"https://github.com/dazcona/mrnet","url":"https://github.com/dazcona/mrnet","frameworks":[]}],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-label-classification-on-mrnet","task":"Multi-Label Classification","dataset_variant":"MRNet","rows":2,"metrics":["Average AUC","AUC on Abnormality (ABN)","AUC on ACL Tear (ACL)","AUC on Meniscus Tear (MEN)","Average Accuracy","Accuracy on Abnormality (ABN)","Accuracy on ACL Tear (ACL)","Accuracy on Meniscus Tear (MEN)"],"first_row_in_archive_order":{"model":"MRNet","paper":"/paper/deep-learning-assisted-diagnosis-for-knee","metrics":{"AUC on ACL Tear (ACL)":"0.915","AUC on Abnormality (ABN)":"0.944","AUC on Meniscus Tear (MEN)":"0.822","Accuracy on ACL Tear (ACL)":"0.867","Accuracy on Abnormality (ABN)":"0.850","Accuracy on Meniscus Tear (MEN)":"0.725","Average AUC":"0.894","Average Accuracy":"0.814"},"code_links":[{"title":"ahmedbesbes/mrnet","url":"https://github.com/ahmedbesbes/mrnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sslm-self-supervised-learning-for-medical","title":"SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data","date":"2021-04-21","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-learning-assisted-diagnosis-for-knee","title":"Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet","date":"2018-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}