Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With
the pressures for improved care and translation of the latest medical advances and …

Despite the growing availability of high-quality public datasets, the lack of training samples
is still one of the main challenges of deep-learning for skin lesion analysis. Generative …

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …

Deep learning-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …

Generative adversarial networks (GANs) are state-of-the-art neural network models used to
synthesise images and other data. GANs brought a considerable improvement to the quality …

Synthetic data are becoming a critical tool for building artificially intelligent systems.
Simulators provide a way of generating data systematically and at scale. These data can …

Synthesis of a large set of high-quality medical images with variability in anatomical
representation and image appearance has the potential to provide solutions for tackling the …

Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis
of cardiac function and diagnosis of heart related diseases. While recent deep learning …