  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.
