A Probabilistic Scheme For Representation Learning With Radial Transform Images

Data representation can facilitate training of deep neural network when limited data is available. We have previously proposed the radial transform sampling method as a data representation technique for training neural networks. In this paper, a probabilistic framework to analyze radial transform is presented. To further elaborate it, performance of training deep neural networks on radial transform generated images for semantic segmentation of kidneys in abdominal computed tomography is evaluated. Our results show that the proposed representation method can achieve higher performance than other similar methods by translating a semantic segmentation problem to a classification problem when limited annotated images are available.