activity
20172022
most citedAdversarial Attack on Hierarchical Graph Pooling Neural Networks

22 citations · 41 across the 10 of their papers we have counts for

collaborators

11 papers

cs.LG20221 cited

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

Jun Yu, Zhaoming Kong, Liang Zhan +2

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combi…

cs.LG2022

Functional2Structural: Cross-Modality Brain Networks Representation Learning

Haoteng Tang, Xiyao Fu, Lei Guo +7

MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, su…

cs.CV20214 cited

PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images

Haozhe Jia, Haoteng Tang, Guixiang Ma +4

Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of CO…

cs.CV2021

Boundary-aware Graph Reasoning for Semantic Segmentation

Haoteng Tang, Haozhe Jia, Weidong Cai +3

In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation. Rather than directly construct the graph…

cs.CV20215 cited

Multiplex Graph Networks for Multimodal Brain Network Analysis

Zhaoming Kong, Lichao Sun, Hao Peng +3

In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. The proposed method integrates tens…

cs.LG20204 cited

CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning

Haoteng Tang, Guixiang Ma, Lifang He +2

Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for gr…