# Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation

arXiv: https://arxiv.org/abs/2402.04031 · HTML: https://mlsanity.com/paper/2402.04031/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 7 of 7)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis](https://mlsanity.com/paper/2305.18453/) (2023)
- [SinGAN-Seg: Synthetic training data generation for medical image segmentation](https://mlsanity.com/paper/2107.00471/) (2021)
- [A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation](https://mlsanity.com/paper/2107.12435/) (2021)
- [Mask-conditioned latent diffusion for generating gastrointestinal polyp images](https://mlsanity.com/paper/2304.05233/) (2023)

## Built on by (most cited first, 8 of 8)

- [Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation](https://mlsanity.com/paper/2505.06068/) (2025)
- [MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs](https://mlsanity.com/paper/2504.06897/) (2025)
- [Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion](https://mlsanity.com/paper/2501.16679/) (2025)
- [Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification](https://mlsanity.com/paper/2409.17091/) (2024)
- [LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features](https://mlsanity.com/paper/2408.14977/) (2024)
- [A Conditional Generative Framework for Synthetic Data Augmentation in Segmenting Thin and Elongated Structures in Biological Images](https://mlsanity.com/paper/2512.10334/) (2025)
- [Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation](https://mlsanity.com/paper/2608.07176/) (2026)
- [Beyond the Foreground: FOV-Aware Polyp Image Synthesis via Lesion-Guided Adaptive Mucosal Context Propagation](https://mlsanity.com/paper/2609.19966/) (2026)

## Abstract (arXiv)

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.