Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Variational autoencoders (VAEs) are deep latent space generative models that have been
immensely successful in multiple exciting applications in biomedical informatics such as …

Due to the three-dimensional nature of CT-or MR-scans, generative modeling of medical
images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice …

Abstract Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional
(3D) medical images has great potential that can be extended to many medical applications …

Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac
ultrasound images. However, despite recent successes according to which the intra …

Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …

Generative Adversarial Networks (GAN) are emerging as an exciting training paradigm
which promises a step improvement to the impressive feature learning capabilities of deep …

Introduction Studying the spatiotemporal patterns of amyloid accumulation in the brain over
time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography …

Abstract Background Generative Adversarial Networks (GANs) can synthesize brain images
from image or noise input. So far, the gold standard for assessing the quality of the …