  Voxelwise classification approaches are popular and effective methods for
tissue quantification in brain magnetic resonance imaging (MRI) scans. However,
generalization of these approaches is hampered by large differences between
sets of MRI scans such as differences in field strength, vendor or acquisition
protocols. Due to this acquisition related variation, classifiers trained on
data from a specific scanner fail or under-perform when applied to data that
was acquired differently. In order to address this lack of generalization, we
propose a Siamese neural network (MRAI-net) to learn a representation that
minimizes the between-scanner variation, while maintaining the contrast between
brain tissues necessary for brain tissue quantification. The proposed MRAI-net
was evaluated on both simulated and real MRI data. After learning the MR
acquisition invariant representation, any supervised classification model that
uses feature vectors can be applied. In this paper, we provide a proof of
principle, which shows that a linear classifier applied on the MRAI
representation is able to outperform supervised convolutional neural network
classifiers for tissue classification when little target training data is
available.
