# Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients

arXiv: https://arxiv.org/abs/2101.06772 · HTML: https://mlsanity.com/paper/2101.06772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 17 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https://mlsanity.com/paper/1807.06358/) (2018)
- [Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1908.02498/) (2019)
- [q-Space Novelty Detection with Variational Autoencoders](https://mlsanity.com/paper/1806.02997/) (2018)
- [Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/1811.02942/) (2018)
- [Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET](https://mlsanity.com/paper/1901.05733/) (2019)
- [Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks](https://mlsanity.com/paper/1803.09172/) (2018)
- [Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE](https://mlsanity.com/paper/2002.05692/) (2020)

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

- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)

## Abstract (arXiv)

Multiple Sclerosis (MS) and microvascular leukoencephalopathy are two distinct neurological conditions, the first caused by focal autoimmune inflammation in the central nervous system, the second caused by chronic white matter damage from atherosclerotic microvascular disease. Both conditions lead to signal anomalies on Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images, which can be distinguished by an expert neuroradiologist, but which can look very similar to the untrained eye as well as in the early stage of both diseases. In this paper, we attempt to train a 3-dimensional deep neural network to learn the specific features of both diseases in an unsupervised manner. For this manner, in a first step we train a generative neural network to create artificial MR images of both conditions with approximate explicit density, using a mixed dataset of multiple sclerosis, leukoencephalopathy and healthy patients containing in total 5404 volumes of 3096 patients. In a second step, we distinguish features between the different diseases in the latent space of this network, and use them to classify new data.