Photorealistic Colonoscopy Image Generation

Abstract

This paper explores controlled synthetic colonoscopy image generation with polyps using ControlNet and depth maps. Generated images augment training of a polyp segmentation network, reducing reliance on real clinical data. PolypMini, the best generated dataset, achieves competitive results for standard and small morphologies compared with real-data training.