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Found 7 papers, 4 papers with code
Modality Bank: Learn multi-modality images across data centers without sharing medical data
Multi-modality images have been widely used and provide comprehensive information for medical image analysis.
DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation.
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.
Learn distributed GAN with Temporary Discriminators
Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators?
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).
Non-Local Graph-Based Prediction For Reversible Data Hiding In Images
If sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either a quadratic Laplacian regularizer or a graph total variation (GTV) term as signal prior.
