  Neuroimaging studies based on magnetic resonance imaging (MRI) typically
employ rigorous forms of preprocessing. Images are spatially normalized to a
standard template using linear and non-linear transformations. Thus, one can
assume that a patch at location (x, y, height, width) contains the same brain
region across the entire data set. Most analyses applied on brain MRI using
convolutional neural networks (CNNs) ignore this distinction from natural
images. Here, we suggest a new layer type called patch individual filter (PIF)
layer, which trains higher-level filters locally as we assume that more
abstract features are locally specific after spatial normalization. We evaluate
PIF layers on three different tasks, namely sex classification as well as
either Alzheimer's disease (AD) or multiple sclerosis (MS) detection. We
demonstrate that CNNs using PIF layers outperform their counterparts in
several, especially low sample size settings.
