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

An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging