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The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity.
Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach
Elizabeth R. DeLong, David M. DeLong, and Daniel L. Clarke-Pearson · 1988
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PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals
Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley · 2000
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Utilization of radiology services in the United States: Levels and trends in modalities, regions, and populations
Mythreyi Bhargavan and Jonathan H Sunshine · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Fast implementation of DeLong’s algorithm for comparing the areas under correlated receiver operating characteristic curves
X. Sun and W. Xu · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans
Jie-Zhi Cheng, Dong Ni, Yi-Hong Chou, Jing Qin, Chui-Mei Tiu, Yeun-Chung Chang, Chiun-Sheng Huang, Dinggang Shen, and Chung-Ming Chen · 2016
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Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
Hoo-Chang Shin, Holger R Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M Summers · 2016
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CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al · 2017
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Deep learning: A primer for radiologists
Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, and An Tang · 2017
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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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ChestX-ray8: Hospital-scale chest X-Ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Convolutional neural networks: an overview and application in radiology
Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi · 2018
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Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images
Xiaocong Chen, Lina Yao, Tao Zhou, Jinming Dong, and Yu Zhang · 2020
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Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation
Amine Amyar, Romain Modzelewski, Hua Li, and Su Ruan · 2020
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Diagnosis of COVID-19 pneumonia using chest radiography: Value of artificial intelligence
Ran Zhang, Xin Tie, Zhihua Qi, Nicholas B. Bevins, Chengzhu Zhang, Dalton Griner, Thomas K. Song, Jeffrey D. Nadig, Mark L. Schiebler, John W. Garrett, Ke Li, Scott B. Reeder, and Guang-Hong Chen · 2020
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Deep learning COVID-19 features on CXR using limited training data sets
Yujin Oh, Sangjoon Park, and Jong Chul Ye · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
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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.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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.
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng · 2019
Cited alongside, same era.
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Alistair EW Johnson, Tom J Pollard, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G Mark, Seth J Berkowitz, and Steven Horng · 2019
Cited alongside, same era.
Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Jan Witowski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, and Krzysztof J. Geras · 2020
Cited alongside, same era.
Combining initial radiographs and clinical variables improves deep learning prognostication of patients with COVID-19 from the emergency department
Young Joon Kwon, Danielle Toussie, Mark Finkelstein, Mario A. Cedillo, Samuel Z. Maron, Sayan Manna, Nicholas Voutsinas, Corey Eber, Adam Jacobi, Adam Bernheim, Yogesh Sean Gupta, Michael S. Chung, Zahi A. Fayad, Benjamin Glicksberg, Eric K. Oermann, and Anthony B. Costa · 2020
Cited alongside, same era.
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Moco pretraining improves representation and transferability of chest x-ray models, 2020
Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng, and Pranav Rajpurkar · 2020
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PyTorch distributed: Experiences on accelerating data parallel training
Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, and Soumith Chintala · 2020
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Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Laure Wynants, Ben Van Calster, Gary S Collins, Richard D Riley, Georg Heinze, Ewoud Schuit, Marc M J Bonten, Darren L Dahly, Johanna A A Damen, Thomas P A Debray, Valentijn M T de Jong, Maarten De Vos, Paula Dhiman, Maria C Haller, Michael O Harhay, Liesbet Henckaerts, Pauline Heus, Nina Kreuzberger, Anna Lohmann, Kim Luijken, Jie Ma, Glen P Martin, Constanza L Andaur Navarro, Johannes B Reitsma, Jamie C Sergeant, Chunhu Shi, Nicole Skoetz, Luc J M Smits, Kym I E Snell, Matthew Sperrin, René Spijker, Ewout W Steyerberg, Toshihiko Takada, Ioanna Tzoulaki, Sander M J van Kuijk, Florien S van Royen, Jan Y Verbakel, Christine Wallisch, Jack Wilkinson, Robert Wolff, Lotty Hooft, Karel G M Moons, and Maarten van Smeden · 2020
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An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Yiqiu Shen, Nan Wu, Jason Phang, Jungkyu Park, Kangning Liu, Sudarshini Tyagi, Laura Heacock, S Gene Kim, Linda Moy, Kyunghyun Cho, et al · 2021
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