This paper presents a comprehensive systematic review of generative models (GANs, VAEs,
DMs, and LLMs) used to synthesize various medical data types, including imaging …

Improving the quality of medical images is crucial for accurate clinical diagnosis; however,
medical images are often disrupted by various types of noise, posing challenges to the …

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms
can provide a clinically feasible alternative to classic algorithms for synthetic Computer …

Cervical cancer remains a significant global health concern. Given the disparity between
limited medical resources and the requisite professional personnel, the coverage of cervical …

Diffusion models have achieved impressive performance on various image generation
tasks, including image super-resolution. Despite their impressive performance, diffusion …

Generative models are used as an alternative data augmentation technique to alleviate the
data scarcity problem faced in the medical imaging field. Diffusion models have gathered …

This paper intends to provide the reader with an overview of the main processes that are
introducing artificial intelligence (AI) into healthcare services. The first part is organized …

Image generation is a prevailing technique for clinical data augmentation for advancing
diagnostic accuracy and reducing healthcare disparities. Diffusion Model (DM) has become …

Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis
and tumor recurrence tracking. Hematoxylin excels at highlighting nuclei, whereas eosin …

With the increased interest in utilizing machine learning techniques for microwave breast
imaging there is an associated increase in the demand for anthropomorphically accurate …