Multi-modality images have been widely used and provide comprehensive information for medical image analysis.
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation.
A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.
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?
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).
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.