51 research outputs found
Low-Rank Projections of GCNs Laplacian
In this work, we study the behavior of standard models for community
detection under spectral manipulations. Through various ablation experiments,
we evaluate the impact of bandpass filtering on the performance of a GCN: we
empirically show that most of the necessary and used information for nodes
classification is contained in the low-frequency domain, and thus contrary to
images, high frequencies are less crucial to community detection. In
particular, it is sometimes possible to obtain accuracies at a state-of-the-art
level with simple classifiers that rely only on a few low frequencies
Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity
Functional connectivity network (FCN) data from functional magnetic resonance
imaging (fMRI) is increasingly used for the diagnoses of brain disorders.
However, state-of-the-art studies used to build the FCN using a single brain
parcellation atlas at a certain spatial scale, which largely neglected
functional interactions across different spatial scales in hierarchical
manners. In this study, we propose a novel framework to perform multiscale FCN
analysis for brain disorder diagnosis. We first use a set of well-defined
multiscale atlases to compute multiscale FCNs. Then, we utilize biologically
meaningful brain hierarchical relationships among the regions in multiscale
atlases to perform nodal pooling across multiple spatial scales, namely
"Atlas-guided Pooling". Accordingly, we propose a Multiscale-Atlases-based
Hierarchical Graph Convolutional Network (MAHGCN), built on the stacked layers
of graph convolution and the atlas-guided pooling, for a comprehensive
extraction of diagnostic information from multiscale FCNs. Experiments on
neuroimaging data from 1792 subjects demonstrate the effectiveness of our
proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal
stage of AD (i.e., mild cognitive impairment [MCI]), as well as autism spectrum
disorder (ASD), with accuracy of 88.9%, 78.6%, and 72.7% respectively. All
results show significant advantages of our proposed method over other competing
methods. This study not only demonstrates the feasibility of brain disorder
diagnosis using resting-state fMRI empowered by deep learning, but also
highlights that the functional interactions in the multiscale brain hierarchy
are worth being explored and integrated into deep learning network
architectures for better understanding the neuropathology of brain disorders
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Co-saliency detection aims to discover the common and salient foregrounds
from a group of relevant images. For this task, we present a novel adaptive
graph convolutional network with attention graph clustering (GCAGC). Three
major contributions have been made, and are experimentally shown to have
substantial practical merits. First, we propose a graph convolutional network
design to extract information cues to characterize the intra- and interimage
correspondence. Second, we develop an attention graph clustering algorithm to
discriminate the common objects from all the salient foreground objects in an
unsupervised fashion. Third, we present a unified framework with
encoder-decoder structure to jointly train and optimize the graph convolutional
network, attention graph cluster, and co-saliency detection decoder in an
end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency
detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method
obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202
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