  In this report we present an unsupervised image registration framework, using
a pre-trained deep neural network as a feature extractor. We refer this to
zero-shot learning, due to nonoverlap between training and testing dataset
(none of the network modules in the processing pipeline were trained
specifically for the task of medical image registration). Highlights of our
technique are: (a) No requirement of a training dataset (b) Keypoints
i.e.locations of important features are automatically estimated (c) The number
of key points in this model is fixed and can possibly be tuned as a
hyperparameter. (d) Uncertaintycalculation of the proposed, transformation
estimates (e) Real-time registration of images. Our technique was evaluated on
BraTS, ALBERT, and collaborative hospital Brain MRI data. Results suggest that
the method proved to be robust for affine transformation models and the results
are practically instantaneous, irrespective of the size of the input image
