  In machine learning, novelty detection is the task of identifying novel
unseen data. During training, only samples from the normal class are available.
Test samples are classified as normal or abnormal by assignment of a novelty
score. Here we propose novelty detection methods based on training variational
autoencoders (VAEs) on normal data. Since abnormal samples are not used during
training, we define novelty metrics based on the (partially complementary)
assumptions that the VAE is less capable of reconstructing abnormal samples
well; that abnormal samples more strongly violate the VAE regularizer; and that
abnormal samples differ from normal samples not only in input-feature space,
but also in the VAE latent space and VAE output. These approaches, combined
with various possibilities of using (e.g. sampling) the probabilistic VAE to
obtain scalar novelty scores, yield a large family of methods. We apply these
methods to magnetic resonance imaging, namely to the detection of
diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple
sclerosis patients, i.e. to detect multiple sclerosis lesions without using any
lesion labels for training. Many of our methods outperform previously proposed
q-space novelty detection methods. We also evaluate the proposed methods on the
MNIST handwritten digits dataset and show that many of them are able to
outperform the state of the art.
