PRLx-GAN

PRLx-GAN

Repository for Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis published in Synthetic Data at CVPR 2025.

Summary

Paramagnetic rim lesions (PRLs) are a rare but highly prognostic lesion subtype in multiple sclerosis, visible only on susceptibility ($\chi$) contrasts. This work presents a generative framework to: <br/>

• Synthesize new rim lesion maps that address class imbalance in training data <br/>
• Enable a novel denoising method to resolve radiologist disagreements on noisy labels, "ambiguous rim lesions". <br/>

<div style="width:50%; margin: auto;">

<i>Latent projection iterations to identify the denoised rim lesion from a "noisy" rim lesion.</i>

</div>

Contents

Uncurated synthetic rim lesion susceptibilities can be found in png <br/> Pretrained weights are located in net <br/>

Preliminaries

To download the pretrained weights, ensure Git Large File Service is installed

sudo apt-get install git-lfs
git lfs install

The main.sh script will skip retraining unless \your\QSM\data is replaced by a valid path

Installation

Clone the repository with

git clone https://github.com/agr78/PRLx-GAN.git

Navigate to the repository

cd PRLx-GAN

Run the setup script

source ./src/main.sh

Wait...then check the generated and denoised outputs

cd ./out

Publications

If this code is used, please cite the following:

Conference Paper: A. G. Roberts et al., "Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis," Synthetic Data for Computer Vision at CVPR, 2025.

BibTex

@inproceedings{
roberts2025synthetic,
title={Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis},
author={Alexandra Grace Roberts and Ha Manh Luu and Mert Sisman and Alexey V. Dimov and Ceren Tozlu and Ilhami Kovanlikaya and Susan Gauthier and Thanh D. Nguyen and Yi Wang},
booktitle={Synthetic Data for Computer Vision Workshop @ CVPR 2025},
year={2025},
url={https://openreview.net/forum?id=wFkiqB5spT}
}

Acknowledgements

This method relies on the StyleGAN2-ADA architecture developed by @tkarras.

Contact

Please direct questions to Alexandra Roberts at [email protected].