Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

Synthesis of Gadolinium-enhanced MRI for Multiple Sclerosis patients using Generative Adversarial Network

Multile Sclerosis is detected by MRI using contrast agent Gadolinium (GAD). But this chemical agent has side effects on patients and many people are allergic to it. As a broader goal of this project, we aim to use Generative Adversarial Network to generate multiple possible gadolinium enhancements for the same patient. This will associate a measure of uncertainty to GAD activity. Actual GAD MRI...

Generative Multi-Adversarial Networks

Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the Generative Multi-Adversarial Network (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GM...

Wasserstein Generative Adversarial Network

Recent advances in deep generative models give us new perspective on modeling highdimensional, nonlinear data distributions. Especially the GAN training can successfully produce sharp, realistic images. However, GAN sidesteps the use of traditional maximum likelihood learning and instead adopts an two-player game approach. This new training behaves very differently compared to ML learning. Ther...

Controllable Generative Adversarial Network

Although it is recently introduced, in last few years, generative adversarial network (GAN) has been shown many promising results to generate realistic samples. However, it is hardly able to control generated samples since input variables for a generator are from a random distribution. Some attempts have been made to control generated samples from GAN, but they have shown moderate results. Furt...

Sequence synthesis with Conditional Generative Adversarial Recurrent Neural Networks