Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced
  Segmentation
Zolnamar Dorjsembe Hsing-Kuo Pao Furen Xiao
This study introduces Polyp-DDPM, a diffusion-based method for generating
realistic images of polyps conditioned on masks, aimed at enhancing the
segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the
challenges of data limitations, high annotation costs, and privacy concerns
associated with medical images. By conditioning the diffusion model on
segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM
outperforms state-of-the-art methods in terms of image quality (achieving a
Frechet Inception Distance (FID) score of 78.47, compared to scores above
83.79) and segmentation performance (achieving an Intersection over Union (IoU)
of 0.7156, versus less than 0.6694 for synthetic images from baseline models
and 0.7067 for real data). Our method generates a high-quality, diverse
synthetic dataset for training, thereby enhancing polyp segmentation models to
be comparable with real images and offering greater data augmentation
capabilities to improve segmentation models. The source code and pretrained
weights for Polyp-DDPM are made publicly available at
https://github.com/mobaidoctor/polyp-ddpm.