Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

MRI and CT are most widely used medical imaging modalities. It is often necessary to
acquire multi-modality images for diagnosis and treatment such as radiotherapy planning …

Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …

Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …

The basic Generative Adversarial Networks (GAN) model is composed of the input vector,
generator, and discriminator. Among them, the generator and discriminator are implicit …

Generative adversarial networks (GANs) are unsupervised deep learning approach in the
computer vision community which has gained significant attention from the last few years in …

A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …