Synthetic Medical Image

Synthetic medical image generation uses deep learning, particularly generative adversarial networks (GANs) and diffusion models, to create realistic artificial medical images for addressing data scarcity, privacy concerns, and annotation limitations in healthcare. Current research focuses on improving the realism and diversity of these synthetic images, developing robust quality assessment metrics (both reference and non-reference based), and exploring methods to align synthetic data with clinical knowledge. This field is significant because it offers a powerful tool for augmenting training datasets, enabling the development of more accurate and robust medical image analysis algorithms, ultimately improving diagnostic capabilities and patient care.