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We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Effect of clinical history data on chest film interpretation-direction or distraction
Potchen, EJ, Gard, JW, Lazar, P, Lahaie, P, and Andary, M · 1979
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The effect of comparison films upon resident interpretation of pediatric chest radiographs
Berbaum, K, Franken Jr, EA, and Smith, WL · 1985
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Reliability of the chest radiograph in the diagnosis of lower respiratory infections in young children
Davies, H Dele, Wang, Elaine E-l, Manson, David, Babyn, Paul, and Shuckett, Bruce · 1996
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Imaging of pneumonia: trends and algorithms
Franquet, T · 2001
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Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children
WHO · 2001
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Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections
Hopstaken, RM, Witbraad, T, Van Engelshoven, JMA, and Dinant, GJ · 2004
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Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies
Cherian, Thomas, Mulholland, E Kim, Carlin, John B, Ostensen, Harald, Amin, Ruhul, Campo, Margaret de, Greenberg, David, Lagos, Rosanna, Lucero, Marilla, Madhi, Shabir A, et al · 2005
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Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
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Mortality prediction in community-acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores
Aydogdu, M, Ozyilmaz, E, Aksoy, Handan, Gursel, G, and Ekim, Numan · 2010
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White paper report of the rad-aid conference on international radiology for developing countries: identifying challenges, opportunities, and strategies for imaging services in the developing world
Mollura, Daniel J, Azene, Ezana M, Starikovsky, Anna, Thelwell, Aduke, Iosifescu, Sarah, Kimble, Cary, Polin, Ann, Garra, Brian S, DeStigter, Kristen K, Short, Brad, et al · 2010
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Variability in the interpretation of chest radiographs for the diagnosis of pneumonia in children
Neuman, Mark I, Lee, Edward Y, Bixby, Sarah, Diperna, Stephanie, Hellinger, Jeffrey, Markowitz, Richard, Servaes, Sabah, Monuteaux, Michael C, and Shah, Samir S · 2012
Cited alongside, same era.
Interpretation of plain chest roentgenogram
Raoof, Suhail, Feigin, David, Sung, Arthur, Raoof, Sabiha, Irugulpati, Lavanya, and Rosenow, Edward C · 2012
Cited alongside, same era.
Community-acquired pneumonia: identification and evaluation of nonresponders
Gonçalves-Pereira, João, Conceição, Catarina, and Póvoa, Pedro · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
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.
URL https://www.cdc.gov/features/pneumonia/index.html
CDC, 2017 · 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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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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Added value of computer-aided ct image features for early lung cancer diagnosis with small pulmonary nodules: A matched case-control study
Huang, Peng, Park, Seyoun, Yan, Rongkai, Lee, Junghoon, Chu, Linda C, Lin, Cheng T, Hussien, Amira, Rathmell, Joshua, Thomas, Brett, Chen, Chen, et al · 2017
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Abnormality detection and localization in chest x-rays using deep convolutional neural networks
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 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.
2015 rad-aid conference on international radiology for developing countries: The evolving global radiology landscape
Kesselman, Andrew, Soroosh, Garshasb, Mollura, Daniel J, and Group, RAD-AID Conference Writing · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Zhou, Bolei, Khosla, Aditya, Lapedriza, Agata, Oliva, Aude, and Torralba, Antonio · 2016
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Islam, Mohammad Tariqul, Aowal, Md Abdul, Minhaz, Ahmed Tahseen, and Ashraf, Khalid · 2017
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Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks
Lakhani, Paras and Sundaram, Baskaran · 2017
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Cardiologist-level arrhythmia detection with convolutional neural networks
Rajpurkar, Pranav, Hannun, Awni Y, Haghpanahi, Masoumeh, Bourn, Codie, and Ng, Andrew Y · 2017
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Wang, Xiaosong, Peng, Yifan, Lu, Le, Lu, Zhiyong, Bagheri, Mohammadhadi, and Summers, Ronald M · 2017
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Learning to diagnose from scratch by exploiting dependencies among labels
Yao, Li, Poblenz, Eric, Dagunts, Dmitry, Covington, Ben, Bernard, Devon, and Lyman, Kevin · 2017
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