  In this work, we present a novel unsupervised image registration algorithm.
It is differentiable end-to-end and can be used for both multi-modal and
mono-modal registration. This is done using mutual information (MI) as a
metric. The novelty here is that rather than using traditional ways of
approximating MI, we use a neural estimator called MINE and supplement it with
matrix exponential for transformation matrix computation. This leads to
improved results as compared to the standard algorithms available
out-of-the-box in state-of-the-art image registration toolboxes.
