Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models
Abstract
We introduce a new technique for generating retinal fundus images that have anatomically accurate vascular structures, using diffusion models. We generate artery/vein masks to create the vascular structure, which we then condition to produce retinal fundus images. The proposed method can generate high-quality images with more realistic vascular structures and can create a diverse range of images based on the strengths of the diffusion model. We present quantitative evaluations that demonstrate the performance improvement using our method for data augmentation on vessel segmentation and artery/vein classification. We also present Turing test results by clinical experts, showing that our generated images are difficult to distinguish with real images. We believe that our method can be applied to construct stand-alone datasets that are irrelevant of patient privacy.
Cite
Text
Go et al. "Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00239Markdown
[Go et al. "Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/go2024cvprw-generation/) doi:10.1109/CVPRW63382.2024.00239BibTeX
@inproceedings{go2024cvprw-generation,
title = {{Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models}},
author = {Go, Sojung and Ji, Younghoon and Park, Sang Jun and Lee, Soochahn},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {2335-2344},
doi = {10.1109/CVPRW63382.2024.00239},
url = {https://mlanthology.org/cvprw/2024/go2024cvprw-generation/}
}