  Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is
comparably homogeneous due to (1) the uniform structure of the brain and (2)
additional efforts to spatially normalize the data to a standard template using
linear and non-linear transformations. Convolutional neural networks (CNNs), in
contrast, have been specifically designed for highly heterogeneous data, such
as natural images, by sliding convolutional filters over different positions in
an image. Here, we suggest a new CNN architecture that combines the idea of
hierarchical abstraction in neural networks with a prior on the spatial
homogeneity of neuroimaging data: Whereas early layers are trained globally
using standard convolutional layers, we introduce for higher, more abstract
layers patch individual filters (PIF). By learning filters in individual image
regions (patches) without sharing weights, PIF layers can learn abstract
features faster and with fewer samples. We thoroughly evaluated PIF layers for
three different tasks and data sets, namely sex classification on UK Biobank
data, Alzheimer's disease detection on ADNI data and multiple sclerosis
detection on private hospital data. We demonstrate that CNNs using PIF layers
result in higher accuracies, especially in low sample size settings, and need
fewer training epochs for convergence. To the best of our knowledge, this is
the first study which introduces a prior on brain MRI for CNN learning.
