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Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases.
A learning method for the class imbalance problem with medical data sets
Der-Chiang Li, Chiao-Wen Liu, and Susan C Hu · 2010
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A few useful things to know about machine learning
Pedro Domingos · 2012
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Epidemiology and classification of bone tumors
Alessandro Franchi · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Analysis of sampling techniques for imbalanced data: An n= 648 adni study
Rashmi Dubey, Jiayu Zhou, Yalin Wang, Paul M Thompson, Jieping Ye, Alzheimer’s Disease Neuroimaging Initiative, et al · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Differential data augmentation techniques for medical imaging classification tasks
Zeshan Hussain, Francisco Gimenez, Darvin Yi, and Daniel Rubin · 2017
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Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia Newcombe, Joanna Simpson, Andrew Kane, David Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, et al · 2017
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Medical image data and datasets in the era of machine learning—whitepaper from the 2016 c-mimi meeting dataset session
Marc D Kohli, Ronald M Summers, and J Raymond Geis · 2017
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Unsupervised image-to-image translation networks
Ming-Yu Liu, Thomas Breuel, and Jan Kautz · 2017
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Dilated residual networks
Fisher Yu, Vladlen Koltun, and Thomas A Funkhouser · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan · 2018
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Scargan: Chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans
Felix Lau, Tom Hendriks, Jesse Lieman-Sifry, Sean Sall, and Dan Golden · 2018
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Bagan: Data augmentation with balancing gan
Giovanni Mariani, Florian Scheidegger, Roxana Istrate, Costas Bekas, and Cristiano Malossi · 2018
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Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen · 2017
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Alvin Rajkomar, Sneha Lingam, Andrew G Taylor, Michael Blum, and John Mongan · 2017
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Deep mr to ct synthesis using unpaired data
Jelmer M Wolterink, Anna M Dinkla, Mark HF Savenije, Peter R Seevinck, Cornelis AT van den Berg, and Ivana Išgum · 2017
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Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks
Hojjat Salehinejad, Shahrokh Valaee, Tim Dowdell, Errol Colak, and Joseph Barfett · 2018
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Medical image synthesis for data augmentation and anonymization using generative adversarial networks
Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine P Andriole, and Mark Michalski · 2018
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Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation
Yuan Xue, Tao Xu, Han Zhang, L Rodney Long, and Xiaolei Huang · 2018
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