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In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).
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Federated learning: Strategies for improving communication efficiency
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Dong Yang, Daguang Xu, S Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, and Dorin Comaniciu · 2017
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Dagan: deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features
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Federated learning for mobile keyboard prediction
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Distributed learning without distress: Privacy-preserving empirical risk minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2018
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Irene Papanicolas, Liana R Woskie, and Ashish K Jha · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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Yuan Xue, Tao Xu, Han Zhang, L Rodney Long, and Xiaolei Huang · 2018
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Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Ke Yan, Xiaosong Wang, Le Lu, and Ronald M Summers · 2018
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Ct-gan: Malicious tampering of 3d medical imagery using deep learning
Yisroel Mirsky, Tom Mahler, Ilan Shelef, and Yuval Elovici · 2019
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Improving nuclei/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss
Hui Qu, Zhennan Yan, Gregory M Riedlinger, Subhajyoti De, and Dimitris N Metaxas · 2019
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Taming the noisy gradient: train deep neural networks with small batch sizes
Yikai Zhang, Hui Qu, Chao Chen, and Dimitris Metaxas · 2019
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Local regularizer improves generalization
Yikai Zhang, Hui Qu, and Dimitris Metaxas1 Chao Chen · 2020
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