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The ability to automatically outline anomalies in brain Magnetic Resonance Images (MRIs) is of great importance in computer-aided diagnosis. Unsupervised anomaly detection methods work primarily by learning the distribution of healthy images and identifying abnormal tissues as outliers. In this paper, we propose a slice-wise detection method which first trains a pair of autoencoders on two different datasets, one with healthy individuals and the other one with images of both normal and tumoural tissues. Next, it classifies slices based on the distance in the latent space between the encoding of the image and the encoding of the reconstruction obtained through the autoencoder trained on healthy images only. We validate our approach with a series of preliminary experiments on the HCP and BRATS-2015 datasets, showing the capability of the proposed method to classify brain MRIs into healthy and unhealthy.

Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder