74 citations · 162 across the 36 of their papers we have counts for
7 papers · 1 filter
Topological Learning for Brain Networks
Tananun Songdechakraiwut, Moo K. Chung
This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology. Such challenging task is made possi…
Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering
Shih-Gu Huang, Moo K. Chung, Anqi Qiu +1
This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Lapla…
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering
Shih-Gu Huang, Moo K. Chung, Anqi Qiu +1
Deep neural networks have recently been recognized as one of the powerful learning techniques in computer vision and medical image analysis. Trained deep neural networks need to be…
Sparse Network Modeling
Moo K. Chung
There have been many attempts to identify high-dimensional network features via multivariate approaches. Specifically, when the number of voxels or nodes, denoted as p, are substan…
Introduction to Random Fields
Moo K. Chung
General linear models (GLM) are often constructed and used in statistical inference at the voxel level in brain imaging. In this paper, we explore the basics of random fields and t…
Diffusion Equations for Medical Images
Moo K. Chung
In brain imaging, the image acquisition and processing processes themselves are likely to introduce noise to the images. It is therefore imperative to reduce the noise while preser…