Generative adversarial networks (GAN) represent two deep learning (DL) models positioned
in an adversarial manner to generate and evaluate images. This area of research promises …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

Abstract Generative Adversarial Networks (GANs) have gained significant attention in the
field of image synthesis, particularly in computer vision. GANs consist of a generative model …

Generative Adversarial Networks are a class of artificial intelligence algorithms that consist
of a generator and a discriminator trained simultaneously through adversarial training. GANs …

In biomedical image analysis, the applicability of deep learning methods is directly impacted
by the quantity of image data available. This is due to deep learning models requiring large …

Medical imaging has become integral to modern healthcare, enabling non-invasive
visualization and assessment of anatomical structures. However, medical imaging datasets …

The identification of lesion within medical image data is necessary for diagnosis, treatment
and prognosis. Segmentation and classification approaches are mainly based on …

In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for various imaging related tasks such as artificial image generation to support AI training …

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 …

Recent years have witnessed a rapidly expanding use of artificial intelligence and machine
learning in medical imaging. Generative adversarial networks (GANs) are techniques to …