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We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either normal or abnormal.
Liability of interpreting too many radiographs
Berlin, Leonard · 2000
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The digital database for screening mammography
Heath, Michael, Bowyer, Kevin, Kopans, Daniel, Moore, Richard, and Kegelmeyer, W Philip · 2000
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Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
Shiraishi, Junji, Katsuragawa, Shigehiko, Ikezoe, Junpei, Matsumoto, Tsuneo, Kobayashi, Takeshi, Komatsu, Ken-ichi, Matsui, Mitate, Fujita, Hiroshi, Kodera, Yoshie, and Doi, Kunio · 2000
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Error in radiology
Fitzgerald, Richard · 2001
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Burden of major musculoskeletal conditions
Woolf, Anthony D and Pfleger, Bruce · 2003
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Utilization of radiology services in the united states: levels and trends in modalities, regions, and populations
Bhargavan, Mythreyi and Sunshine, Jonathan H · 2005
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Bone age assessment of children using a digital hand atlas
Gertych, Arkadiusz, Zhang, Aifeng, Sayre, James, Pospiech-Kurkowska, Sylwia, and Huang, HK · 2007
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An update survey of academic radiologists’ clinical productivity
Lu, Ying, Zhao, Shoujun, Chu, Philip W, and Arenson, Ronald L · 2008
Earlier work this paper cites.
Radiologist supply and workload: international comparison
Nakajima, Yasuo, Yamada, Kei, Imamura, Keiko, and Kobayashi, Kazuko · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
Earlier work this paper cites.
Long radiology workdays reduce detection and accommodation accuracy
Krupinski, Elizabeth A, Berbaum, Kevin S, Caldwell, Robert T, Schartz, Kevin M, and Kim, John · 2010
Cited alongside, same era.
Interrater reliability: the kappa statistic
McHugh, Mary L · 2012
Cited alongside, same era.
URL http://www.boneandjointburden.org/2014-report
2017 · 2014
Cited alongside, same era.
Deep speech: Scaling up end-to-end speech recognition
Hannun, Awni, Case, Carl, Casper, Jared, Catanzaro, Bryan, Diamos, Greg, Elsen, Erich, Prenger, Ryan, Satheesh, Sanjeev, Sengupta, Shubho, Coates, Adam, et al · 2014
Cited alongside, same era.
Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Jaeger, Stefan, Candemir, Sema, Antani, Sameer, Wang, Yi-Xiang J, Lu, Pu-Xuan, and Thoma, George · 2014
Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, Pranav, Zhang, Jian, Lopyrev, Konstantin, and Liang, Percy · 2016
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Learning deep features for discriminative localization
Zhou, Bolei, Khosla, Aditya, Lapedriza, Agata, Oliva, Aude, and Torralba, Antonio · 2016
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Artificial intelligence in medicine & imaging: Available labeled medical datasets
AIMI · 2017
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Bejnordi, Babak Ehteshami, Veta, Mitko, van Diest, Paul Johannes, van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen AWM, Hermsen, Meyke, Manson, Quirine F, Balkenhol, Maschenka, et al · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, Andre, Kuprel, Brett, Novoa, Roberto A, Ko, Justin, Swetter, Susan M, Blau, Helen M, and Thrun, Sebastian · 2017
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Kingma, Diederik and Ba, Jimmy · 2014
Cited alongside, same era.
Preparing a collection of radiology examinations for distribution and retrieval
Demner-Fushman, Dina, Kohli, Marc D, Rosenman, Marc B, Shooshan, Sonya E, Rodriguez, Laritza, Antani, Sameer, Thoma, George R, and McDonald, Clement J · 2015
Cited alongside, same era.
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Gulshan, Varun, Peng, Lily, Coram, Marc, Stumpe, Martin C, Wu, Derek, Narayanaswamy, Arunachalam, Venugopalan, Subhashini, Widner, Kasumi, Madams, Tom, Cuadros, Jorge, et al · 2016
Cited alongside, same era.
Densely connected convolutional networks
Huang, Gao, Liu, Zhuang, Weinberger, Kilian Q, and van der Maaten, Laurens · 2016
Cited alongside, same era.
Cardiologist-level arrhythmia detection with convolutional neural networks
Rajpurkar, Pranav, Hannun, Awni Y, Haghpanahi, Masoumeh, Bourn, Codie, and Ng, Andrew Y
Cited in the paper.
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Rajpurkar, Pranav, Irvin, Jeremy, Zhu, Kaylie, Yang, Brandon, Mehta, Hershel, Duan, Tony, Ding, Daisy, Bagul, Aarti, Langlotz, Curtis, Shpanskaya, Katie, et al
Cited in the paper.
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Detecting hip fractures with radiologist-level performance using deep neural networks
Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., and Palmer, L. J · 2017
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Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in ct scans
Grewal, Monika, Srivastava, Muktabh Mayank, Kumar, Pulkit, and Varadarajan, Srikrishna · 2017
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Osteoarthritis initiative: a multi-center observational study of men and women
OAI · 2017
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Wang, Xiaosong, Peng, Yifan, Lu, Le, Lu, Zhiyong, Bagheri, Mohammadhadi, and Summers, Ronald M · 2017
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