  Deep learning convolutional neural networks have proved to be a powerful tool
for MRI analysis. In current work, we explore the potential of the deformable
convolutional deep neural network layers for MRI data classification. We
propose new 3D deformable convolutions(d-convolutions), implement them in
VoxResNet architecture and apply for structural MRI data classification. We
show that 3D d-convolutions outperform standard ones and are effective for
unprocessed 3D MR images being robust to particular geometrical properties of
the data. Firstly proposed dVoxResNet architecture exhibits high potential for
the use in MRI data classification.
