  Recent years have witnessed the emergence and increasing popularity of 3D
medical imaging techniques with the development of 3D sensors and technology.
However, achieving geometric invariance in the processing of 3D medical images
is computationally expensive but nonetheless essential due to the presence of
possible errors caused by rigid registration techniques. An alternative way to
analyze medical imaging is by understanding the 3D shapes represented in terms
of point-cloud. Though in the medical imaging community, 3D point-cloud
processing is not a "go-to" choice, it is a canonical way to preserve rotation
invariance. Unfortunately, due to the presence of discrete topology, one can
not use the standard convolution operator on point-cloud. To the best of our
knowledge, the existing ways to do "convolution" can not preserve the rotation
invariance without explicit data augmentation. Therefore, we propose a rotation
invariant convolution operator by inducing topology from hypersphere.
Experimental validation has been performed on publicly available OASIS dataset
in terms of classification accuracy between subjects with (without) dementia,
demonstrating the usefulness of our proposed method in terms of model
complexity, classification accuracy, and last but most important invariance to
rotations.
