Time Series Generative Learning with Application to Brain Imaging Analysis

This paper focuses on the analysis of sequential image data, particularly brain imaging data such as MRI, fMRI, CT, with the motivation of understanding the brain aging process and neurodegenerative diseases. To achieve this goal, we investigate image generation in a time series context. Specifically, we formulate a min-max problem derived from the $f$-divergence between neighboring pairs to learn a time series generator in a nonparametric manner. The generator enables us to generate future images by transforming prior lag-k observations and a random vector from a reference distribution. With a deep neural network learned generator, we prove that the joint distribution of the generated sequence converges to the latent truth under a Markov and a conditional invariance condition. Furthermore, we extend our generation mechanism to a panel data scenario to accommodate multiple samples. The effectiveness of our mechanism is evaluated by generating real brain MRI sequences from the Alzheimer's Disease Neuroimaging Initiative. These generated image sequences can be used as data augmentation to enhance the performance of further downstream tasks, such as Alzheimer's disease detection.

Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution

Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR). However, the network model of these methods is a fully convolutional neural network, which is limit to exploit contextual information over the global region of the input image. In this paper, we discuss a new SR architecture where features are extracted in the low-resolution (LR) space, and then we use a fully connected layer which learns an array of upsampling weights to reconstruct the desired high-resolution (HR) image from the final LR features. By doing so, we effectively exploit global context information over the input image region, whilst maintaining the low computational complexity for the overall SR operation. In addition, we introduce an edge difference constraint into our loss function to pre-serve edges and texture structures. Extensive experiments validate that our meth-od outperforms the existing state-of-the-art methods