Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation
Journal
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Series/Report No.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Journal Volume
2024
Start Page
1-7
ISSN
2694-0604
ISBN
[9798350371499]
Date Issued
2024-07-15
Author(s)
DOI
10.1109/EMBC53108.2024.10782077
Abstract
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 Fréchet Inception Distance (FID) score of 78.47, compared to scores above 95.82) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6828 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.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
conference paper