Purpose This review provides an overview of the current state of artificial intelligence (AI)
technology for automated detection of breast cancer in digital mammography (DM) and …

Background: Generative Adversarial Networks (GANs) are a class of artificial neural
networks capable of generating content such as images, text, and sound. For several years …

Objectives: Data scarcity and domain shifts lead to biased training sets that do not accurately
represent deployment conditions. A related practical problem is cross-modal image …

Latent diffusion models (LDMs) have emerged as a state-of-the-art image generation
method, outperforming previous Generative Adversarial Networks (GANs) in terms of …

This study attempts to synthesize a realistic-looking fundus image from a morphologically
changed vessel structure using the newly proposed sharpening and varying vessels …

Class imbalance remains a large problem in high-throughput omics analyses, causing bias
towards the over-represented class when training machine learning-based classifiers …

Despite its benefits for tumour detection and treatment, the administration of contrast agents
in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues …

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a
sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires …

Breast cancer is a worldwide fatal disease that exists mostly among women. The deep
learning technique has proven its effectiveness, but the performance of the existing deep …

Generative Adversarial Networks (GANs) have exhibited noteworthy advancements across
various applications, including medical imaging. While numerous state-of-the-art Deep …