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The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation.
Deep learning in chest radiography: Detection of findings and presence of change
Ramandeep Singh, Mannudeep K. Kalra, Chayanin Nitiwarangkul, John A. Patti, Fatemeh Homayounieh, Atul Padole, Pooja Rao, Preetham Putha, Victorine V. Muse, Amita Sharma, and Subba R. Digumarthy · 1932
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The first telemedicine link for the british forces
DJ Vassallo, PJ Buxton, JH Kilbey, and M Trasler · 2011
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Image and diagnosis quality of x-ray image transmission via cell phone camera: a project study evaluating quality and reliability
Hans Goost, Johannes Witten, Andreas Heck, Dariusch R Hadizadeh, Oliver Weber, Ingo Gräff, Christof Burger, Mareen Montag, Felix Koerfer, and Koroush Kabir · 2012
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions
Samuel Dodge and Lina Karam · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani and Baskaran Sundaram · 2017
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How far have we come? Artificial intelligence for chest radiograph interpretation
K. Kallianos, J. Mongan, S. Antani, T. Henry, A. Taylor, J. Abuya, and M. Kohli · 2018
Earlier work this paper cites.
Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs
Ju Gang Nam, Sunggyun Park, Eui Jin Hwang, Jong Hyuk Lee, Kwang-Nam Jin, Kun Young Lim, Thienkai Huy Vu, Jae Ho Sohn, Sangheum Hwang, Jin Mo Goo, and others · 2018
Cited alongside, same era.
Computer-aided detection in chest radiography based on artificial intelligence: a survey
Chunli Qin, Demin Yao, Yonghong Shi, and Zhijian Song · 2018
Cited alongside, same era.
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Pranav Rajpurkar, Jeremy Irvin, Robyn L. Ball, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis P. Langlotz, Bhavik N. Patel, Kristen W. Yeom, Katie Shpanskaya, Francis G. Blankenberg, Jayne Seekins, Timothy J. Amrhein, David A. Mong, Safwan S. Halabi, Evan J. Zucker, Andrew Y. Ng, and Matthew P. Lungren · 2018
Cited alongside, same era.
Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Artificial intelligence for point of care radiograph quality assessment
Satyananda Kashyap, Mehdi Moradi, Alexandros Karargyris, Joy T. Wu, Michael Morris, Babak Saboury, Eliot Siegel, and Tanveer Syeda-Mahmood · 2019
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Key challenges for delivering clinical impact with artificial intelligence
Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, and Dominic King · 2019
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Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia
George Shih, Carol C. Wu, Safwan S. Halabi, Marc D. Kohli, Luciano M. Prevedello, Tessa S. Cook, Arjun Sharma, Judith K. Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, Ritu R. Gill, Myrna C.B. Godoy, Stephen Hobbs, Jean Jeudy, Archana Laroia, Palmi N. Shah, Dharshan Vummidi, Kavitha Yaddanapudi, and Anouk Stein · 2019
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The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation
Davide Chicco and Giuseppe Jurman · 2020
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Chexphoto: 10,000+ smartphone photos and synthetic photographic transformations of chest x-rays for benchmarking deep learning robustness, 2020
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 2019
Cited alongside, same era.
Nick A. Phillips, Pranav Rajpurkar, Mark Sabini, Rayan Krishnan, Sharon Zhou, Anuj Pareek, Nguyet Minh Phu, Chris Wang, Andrew Y. Ng, and Matthew P. Lungren · 2020
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Chexpedition: Investigating generalization challenges for translation of chest x-ray algorithms to the clinical setting, 2020
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Phil Chen, Amirhossein Kiani, Jeremy Irvin, Andrew Y. Ng, and Matthew P. Lungren · 2020
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Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems
Zhi Zhen Qin, Melissa S. Sander, Bishwa Rai, Collins N. Titahong, Santat Sudrungrot, Sylvain N. Laah, Lal Mani Adhikari, E. Jane Carter, Lekha Puri, Andrew J. Codlin, and Jacob Creswell · 2045
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