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Medical datasets are often highly imbalanced with over-representation of common medical problems and a paucity of data from rare conditions.
“Mutations in a novel gene lead to kidney tumors, lung wall defects, and benign tumors of the hair follicle in patients with the birt-hogg-dube syndrome,”
Michael L Nickerson, Michelle B Warren, Jorge R Toro, Vera Matrosova, Gladys Glenn, Maria L Turner, Paul Duray, Maria Merino, Peter Choyke, Christian P Pavlovich, et al., · 2002
Earlier work this paper cites.
“Privacy and ownership preserving of outsourced medical data,”
Elisa Bertino, Beng Chin Ooi, Yanjiang Yang, and Robert H Deng, · 2005
Earlier work this paper cites.
“Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance,”
Maciej A Mazurowski, Piotr A Habas, Jacek M Zurada, Joseph Y Lo, Jay A Baker, and Georgia D Tourassi, · 2008
Earlier work this paper cites.
“A learning method for the class imbalance problem with medical data sets,”
Der-Chiang Li, Chiao-Wen Liu, and Susan C Hu, · 2010
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
“Generative adversarial nets,”
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, · 2014
Earlier work this paper cites.
“Recurrent neural networks for sequential phenotype prediction in genomics,”
Farhad Pouladi, Hojjat Salehinejad, and Amir Mohammad Gilani, · 2015
Cited alongside, same era.
“Unsupervised representation learning with deep convolutional generative adversarial networks,”
Alec Radford, Luke Metz, and Soumith Chintala, · 2015
Cited alongside, same era.
“Customer shopping pattern prediction: A recurrent neural network approach,”
Hojjat Salehinejad and Shahryar Rahnamayan, · 2016
Cited alongside, same era.
“Automatic steganographic distortion learning using a generative adversarial network,”
Weixuan Tang, Shunquan Tan, Bin Li, and Jiwu Huang, · 2017
Cited alongside, same era.
“Generative adversarial network-based postfilter for statistical parametric speech synthesis,”
Takuhiro Kaneko, Hirokazu Kameoka, Nobukatsu Hojo, Yusuke Ijima, Kaoru Hiramatsu, and Kunio Kashino, · 2017
Cited alongside, same era.
“Learning representations of emotional speech with deep convolutional generative adversarial networks,”
J. Chang and S. Scherer, · 2017
Closest in time.
“Generative adversarial networks for noise reduction in low-dose ct,”
Jelmer M Wolterink, Tim Leiner, Max A Viergever, and Ivana Isgum, · 2017
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“End-to-end adversarial retinal image synthesis,”
Pedro Costa, Adrian Galdran, Maria Ines Meyer, Meindert Niemeijer, Michael Abràmoff, Ana Maria Mendonça, and Aurélio Campilho, · 2017
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Mark Cicero, Alexander Bilbily, Errol Colak, Tim Dowdell, Bruce Gray, Kuhan Perampaladas, and Joseph Barfett, · 2017
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“Image augmentation using radial transform for training deep neural networks,”
Hojjat Salehinejad, Shahrokh Valaee, Tim Dowdell, and Joseph Barfett, · 2018
Closest in time.
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