Contrast Enhanced

Contrast enhancement in medical imaging aims to improve the visibility of anatomical structures and lesions by using contrast agents that alter tissue signal intensity. Current research focuses on improving the accuracy and efficiency of contrast-enhanced image analysis, employing deep learning models like U-Nets, generative adversarial networks (GANs), and transformer networks for tasks such as segmentation, detection, and image synthesis. These advancements are crucial for improving diagnostic accuracy, reducing the need for contrast agent dosage, and enabling more efficient and less invasive medical procedures, ultimately leading to better patient care.