214 citations · 259 across the 16 of their papers we have counts for
16 papers
Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Reza Shirkavand, Liang Zhan, Heng Huang +2
Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex br…
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…
Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder
Carlo Amodeo, Igor Fortel, Olusola Ajilore +3
Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain. With the advent of connectomics, the primary graphs or networ…
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…
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu +7
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neur…
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…
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…
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…
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…
Deep Representation Learning For Multimodal Brain Networks
Wen Zhang, Liang Zhan, Paul Thompson +1
Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology…
Adversarial Attack on Hierarchical Graph Pooling Neural Networks
Haoteng Tang, Guixiang Ma, Yurong Chen +4
Recent years have witnessed the emergence and development of graph neural networks (GNNs), which have been shown as a powerful approach for graph representation learning in many ta…
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Mengying Sun, Inci M. Baytas, Liang Zhan +2
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroi…
Sex-by-age differences in the resting-state brain connectivity
Sean D. Conrin, Liang Zhan, Zachery D. Morrissey +6
Recently we developed a novel method for assessing the hierarchical modularity of functional brain networks - the probability associated community estimation(PACE). The PACE algori…
Exploring the Human Connectome Topology in Group Studies
Johnson J. G. Keiriz, Liang Zhan, Morris Chukhman +3
Visually comparing brain networks, or connectomes, is an essential task in the field of neuroscience. Especially relevant to the field of clinical neuroscience, group studies that…
The Importance of Being Negative: A serious treatment of non-trivial edges in brain functional connectome
Liang Zhan, Lisanne M. Jenkins, Ouri E. Wolfson +6
Understanding the modularity of fMRI-derived brain networks or connectomes can inform the study of brain function organization. However, fMRI connectomes additionally involve negat…
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
Qingyang Li, Tao Yang, Liang Zhan +6
Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer's disease (AD). Recently, collaborative efforts across different insti…