Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans. Classification from MRI voxels separately typically does not provide independent evidence towards or against a class; the information relevant for classification is only present in the form of complicated multivariate patterns (or "features"). Deep learning solves this problem by learning a sequence of non-linear transformations that result in feature representations that are better suited to classification. Such learned features have been shown to drastically outperform hand-engineered features in computer vision and audio analysis domains. However, applying the deep learning approach to the task of MRI classification is extremely challenging, because it requires a very large amount of data which is currently not available. We propose to instead use a three dimensional scattering transform, which resembles a deep convolutional neural network but has no learnable parameters. Furthermore, the scattering transform linearizes diffeomorphisms (due to e.g. residual anatomical variability in MRI scans), making the different disease states more easily separable using a linear classifier. In experiments on brain morphometry in Alzheimer's disease, and on white matter microstructural damage in HIV, scattering representations are shown to be highly effective for the task of disease classification. For instance, in semi-supervised learning of progressive versus stable MCI, we reach an accuracy of 82.7%. We also present a visualization method to highlight areas that provide evidence for or against a certain class, both on an individual and group level.

We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike other approaches, our formalism can be used by a machine learning algorithm not only during training but also during testing. Moreover, the proposed approach is flexible as it caters for different formats of side information, and we do not constrain the side information to be fed into the input layer of the network. A formalism is presented based on the difference between the neural network loss without and with side information, stating that it is useful when adding side information reduces the loss during the test phase. As a proof of concept we provide experimental results for two datasets, the MNIST dataset of handwritten digits and the House Price prediction dataset. For the experiments we used feedforward neural networks containing two hidden layers, as well as a softmax output layer. For both datasets, side information is shown to be useful in that it improves the classification accuracy significantly.