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In this paper, we propose a distributed machine learning framework for training and inference in machine learning models using distributed data while preserving privacy of the data owner. In the training mode, we deploy an encoder on the end-user device which extracts high level features from input data. The extracted features along with the corresponding annotation are sent to a centralized machine learning server. In the inference mode, the users submit the extracted features from encoder instead of the original data for inference to the server. This approach enables users to contributed in training a machine learning model and use inference services without sharing their original data with the server or a third party. We have studied this approach on MNIST, Fashion, SVHN and CIFAR-10 datasets. The results show high classification accuracy of neural networks, trained with encoded features, and high encryption performance of the encoders.

In this paper, we propose a novel technique for sampling sequential images using a cylindrical transform in a cylindrical coordinate system for kidney semantic segmentation in abdominal computed tomography (CT). The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions. This approach enables us to train contemporary classification deep convolutional neural networks (DCNNs) instead of fully convolutional networks (FCNs) for semantic segmentation. Typical semantic segmentation models segment a sequential set of images (e.g. CT or video) by segmenting each image independently. However, the proposed method not only considers the spatial dependency in the x-y plane, but also the spatial sequential dependency along the z-axis. The results show that classification DCNNs, trained on cylindrical transformed images, can achieve a higher segmentation performance value than FCNs using a limited number of annotated images., Comment: This paper is accepted for presentation at IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP), California, USA, 2018

Efficient training of supervised deep learning models for semantic segmentation requires a massive volume of annotated data. In this paper, we propose an unsupervised semantic segmentation method through the application of a radial transform method in the polar coordinate system to unannotated images. This method generates radial transformed images up to the number of pixels in the input image. Each generated image corresponds to a pixel in the original image, which is a spatial representation of the selected pixel with respect to other pixels, in the polar coordinate system. A dimension reduction method, such as a convolutional autoencoder (CAE), can extract features of these representations for clustering and later, labeling the original image by mapping the labels back to the original image. The advantage of the proposed radial transform technique is that it generates a massive number of training images by sampling pixels in the polar coordinate system from a very limited number of original images in the Cartesian coordinate system. The proposed approach achieved 88.20% accuracy in pixel-level segmentation of left kidney, right kidney, and non-kidney pixels in contrast-enhanced computed tomography (CT) images.