Deep learning (DL) models are becoming pervasive and applicable to computer vision,
image processing, and synthesis problems. The performance of these models is often …

Deep Learning is vital in medical imaging solutions and clinical applications. However,
multiple reasons, such as data scarcity and imbalance in the medical image dataset, cause …

The ability to generate synthetic medical images is useful for data augmentation, domain
transfer, and out-of-distribution detection. However, generating realistic, high-resolution …

The convolutional neural network (CNN) is a promising technique to detect breast cancer
based on mammograms. Training the CNN from scratch, however, requires a large amount …

Deep learning approaches to breast cancer detection in mammograms have recently shown
promising results. However, such models are constrained by the limited size of publicly …

Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

Abstract Background and Objective: In medical imaging, the scarcity of labeled lesion data
has hindered the application of many deep learning algorithms. To overcome this problem …

Modern deep learning methods achieve state-ofthe-art results in many computer vision
tasks. While these methods perform well when trained on large datasets, deep learning …

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools
for generating synthesized images. To determine the clinical utility of synthesized images …

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 …