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.
@article{arxiv.2402.04031,
title = {Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation},
author = {Zolnamar Dorjsembe and Hsing-Kuo Pao and Furen Xiao},
journal= {arXiv preprint arXiv:2402.04031},
year = {2025}
}
Comments
This preprint has been accepted for publication in the proceedings of the IEEE Engineering in Medicine and Biology Society (EMBC 2024). The final published version is available at https://doi.org/10.1109/EMBC53108.2024.10782077. The copyright for this work has been transferred to IEEE