Medical Image Synthesis

Medical image synthesis uses artificial intelligence to generate realistic medical images, primarily aiming to address data scarcity and privacy concerns in healthcare. Current research heavily focuses on generative adversarial networks (GANs) and diffusion models, often incorporating techniques like attention mechanisms, state space modeling, and registration to improve image fidelity and anatomical accuracy, particularly when dealing with misaligned or incomplete data. This field is significant because it enables data augmentation for training robust diagnostic models, facilitates the creation of diverse training datasets for under-represented conditions, and allows for the exploration of counterfactual scenarios in medical research.