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Found 5 papers, 1 papers with code
Gadolinium dose reduction for brain MRI using conditional deep learning
Recently, deep learning (DL)-based methods have been proposed for the computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects while preserving diagnostic value.
Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs
Our numerical experiments show that conditional GANs are suitable for generating images at different GBCA dose levels and can be used to augment datasets for virtual contrast models.
Shared Prior Learning of Energy-Based Models for Image Reconstruction
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-based learning, a patch-based Wasserstein loss functional, and shared prior learning.
On the estimation of the Wasserstein distance in generative models
Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets.
Impact of the latent space on the ability of GANs to fit the distribution
The goal of generative models is to model the underlying data distribution of a sample based dataset.
