# Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis

arXiv: https://arxiv.org/abs/2505.23353 · HTML: https://mlsanity.com/paper/2505.23353/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 61 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 21 of 21)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Inverting The Generator Of A Generative Adversarial Network](https://mlsanity.com/paper/1611.05644/) (2016)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs](https://mlsanity.com/paper/2011.00844/) (2020)
- [SinGAN-Seg: Synthetic training data generation for medical image segmentation](https://mlsanity.com/paper/2107.00471/) (2021)
- [Robustness of Conditional GANs to Noisy Labels](https://mlsanity.com/paper/1811.03205/) (2018)
- [Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review](https://mlsanity.com/paper/2102.01530/) (2021)
- [Prior Image-Constrained Reconstruction using Style-Based Generative Models](https://mlsanity.com/paper/2102.12525/) (2021)
- [Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET](https://mlsanity.com/paper/1901.05733/) (2019)
- [Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation](https://mlsanity.com/paper/2103.00528/) (2021)
- [SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems](https://mlsanity.com/paper/2308.00994/) (2023)
- [A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement](https://mlsanity.com/paper/2106.15575/) (2021)
- [Maximum Spherical Mean Value (mSMV) Filtering for Whole Brain Quantitative Susceptibility Mapping](https://mlsanity.com/paper/2304.11476/) (2023)
- [QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis](https://mlsanity.com/paper/2412.10492/) (2024)
- [Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts](https://mlsanity.com/paper/2502.14281/) (2025)

## Built on by (most cited first, 1 of 1)

- [Spectral Vision Transformer for Efficient Tokenization with Limited Data](https://mlsanity.com/paper/2605.12026/) (2026)

## Abstract (arXiv)

Quantitative susceptibility maps from magnetic resonance images can provide both prognostic and diagnostic information in multiple sclerosis, a neurodegenerative disease characterized by the formation of lesions in white matter brain tissue. In particular, susceptibility maps provide adequate contrast to distinguish between "rim" lesions, surrounded by deposited paramagnetic iron, and "non-rim" lesion types. These paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple sclerosis. Much effort has been devoted to both detection and segmentation of such lesions to monitor longitudinal change. As paramagnetic rim lesions are rare, addressing this problem requires confronting the class imbalance between rim and non-rim lesions. We produce synthetic quantitative susceptibility maps of paramagnetic rim lesions and show that inclusion of such synthetic data improves classifier performance and provide a multi-channel extension to generate accompanying contrasts and probabilistic segmentation maps. We exploit the projection capability of our trained generative network to demonstrate a novel denoising approach that allows us to train on ambiguous rim cases and substantially increase the minority class. We show that both synthetic lesion synthesis and our proposed rim lesion label denoising method best approximate the unseen rim lesion distribution and improve detection in a clinically interpretable manner. We release our code and generated data at https://github.com/agr78/PRLx-GAN upon publication.