Synthetic Polyp

Synthetic polyp generation uses deep learning, particularly diffusion models and generative adversarial networks (GANs), to create realistic images of colorectal polyps for improving computer-aided diagnosis. Current research focuses on enhancing the realism and diversity of these synthetic images, often by conditioning generation on segmentation masks or leveraging inpainting techniques to integrate polyps into diverse endoscopic backgrounds. This work addresses the limitations of real polyp datasets—high annotation costs, privacy concerns, and limited sample diversity—by providing augmented training data for improving the accuracy of polyp detection and segmentation models, ultimately aiding in early colorectal cancer detection.