{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mura-large-dataset-for-abnormality-detection","title":"MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs","arxiv_id":"1712.06957","date":"2017-12-11","proceeding":null,"authors":["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. Ng"],"abstract":"We introduce MURA, a large dataset of musculoskeletal radiographs containing\n40,561 images from 14,863 studies, where each study is manually labeled by\nradiologists as either normal or abnormal. To evaluate models robustly and to\nget an estimate of radiologist performance, we collect additional labels from\nsix board-certified Stanford radiologists on the test set, consisting of 207\nmusculoskeletal studies. On this test set, the majority vote of a group of\nthree radiologists serves as gold standard. We train a 169-layer DenseNet\nbaseline model to detect and localize abnormalities. Our model achieves an\nAUROC of 0.929, with an operating point of 0.815 sensitivity and 0.887\nspecificity. We compare our model and radiologists on the Cohen's kappa\nstatistic, which expresses the agreement of our model and of each radiologist\nwith the gold standard. Model performance is comparable to the best radiologist\nperformance in detecting abnormalities on finger and wrist studies. However,\nmodel performance is lower than best radiologist performance in detecting\nabnormalities on elbow, forearm, hand, humerus, and shoulder studies. We\nbelieve that the task is a good challenge for future research. To encourage\nadvances, we have made our dataset freely available at\nhttps://stanfordmlgroup.github.io/competitions/mura .","url_abs":"http://arxiv.org/abs/1712.06957v4","url_pdf":"http://arxiv.org/pdf/1712.06957v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/Hashir44/muraxray","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/Valentyn1997/xray","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/Youssefares/MURA-Abnormality-Detection-in-Musculoskeletal-Radiographs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/anirudh2019/MURA-xception","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/anirudh2019/MURA-xception-inceptionV2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/desimone/Musculoskeletal-Radiographs-Abnormality-Classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/pyaf/DenseNet-MURA-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/rajkumargithub/densenet.mura","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/romanovar/evaluation_MIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/sparvangada/capstone_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"mura-large-dataset-for-abnormality-detection","repo_url":"https://github.com/ushashwat/MURA-Bone-Abnormality-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[{"slug":"mura","name":"MURA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.06957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.06957"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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