Search Results for author: Liang Zhan

Found 15 papers, 5 papers with code

Constrained Multiview Representation for Self-supervised Contrastive Learning

no code implementations5 Feb 2024 Siyuan Dai, Kai Ye, Kun Zhao, Ge Cui, Haoteng Tang, Liang Zhan

In this work, we introduce a novel approach predicated on representation distance-based mutual information (MI) maximization for measuring the significance of different views, aiming at conducting more efficient contrastive learning and representation disentanglement.

Contrastive Learning Disentanglement +4

Uncertainty Regularized Evidential Regression

1 code implementation3 Jan 2024 Kai Ye, Tiejin Chen, Hua Wei, Liang Zhan

The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated uncertainty.

regression

Incomplete Multimodal Learning for Complex Brain Disorders Prediction

no code implementations25 May 2023 Reza Shirkavand, Liang Zhan, Heng Huang, Li Shen, Paul M. Thompson

Especially in studies of brain diseases, research cohorts may include both neuroimaging data and genetic data, but for practical clinical diagnosis, we often need to make disease predictions only based on neuroimages.

Data Integration

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

1 code implementation23 Sep 2022 Jun Yu, Zhaoming Kong, Liang Zhan, Li Shen, Lifang He

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task.

feature selection regression

Functional2Structural: Cross-Modality Brain Networks Representation Learning

no code implementations6 May 2022 Haoteng Tang, Xiyao Fu, Lei Guo, Yalin Wang, Scott Mackin, Olusola Ajilore, Alex Leow, Paul Thompson, Heng Huang, Liang Zhan

Since brain networks derived from functional and structural MRI describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks is non-trivial.

Disease Prediction Graph Learning +2

Boundary-aware Graph Reasoning for Semantic Segmentation

no code implementations9 Aug 2021 Haoteng Tang, Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia, Liang Zhan

In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.

graph construction Segmentation +1

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

no code implementations9 Aug 2021 Haozhe Jia, Haoteng Tang, Guixiang Ma, Weidong Cai, Heng Huang, Liang Zhan, Yong Xia

In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel.

Computed Tomography (CT) graph construction +3

Multiplex Graph Networks for Multimodal Brain Network Analysis

1 code implementation31 Jul 2021 Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He

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

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

no code implementations10 Dec 2020 Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, Liang Zhan

In this paper, we propose a new interpretable graph pooling framework - CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process.

Graph Classification Graph Representation Learning

Deep Representation Learning For Multimodal Brain Networks

no code implementations19 Jul 2020 Wen Zhang, Liang Zhan, Paul Thompson, Yalin Wang

The higher-order network mappings from brain structural networks to functional networks are learned in the node domain.

Anatomy Graph Representation Learning

Adversarial Attack on Hierarchical Graph Pooling Neural Networks

no code implementations23 May 2020 Haoteng Tang, Guixiang Ma, Yurong Chen, Lei Guo, Wei Wang, Bo Zeng, Liang Zhan

However, most of the existing work in this area focus on the GNNs for node-level tasks, while little work has been done to study the robustness of the GNNs for the graph classification task.

Adversarial Attack General Classification +3

Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases

1 code implementation ICLR 2018 Mengying Sun, Inci M. Baytas, Liang Zhan, Zhangyang Wang, Jiayu Zhou

Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients.

Multi-Task Learning regression

Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions

no code implementations19 Aug 2016 Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang

To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.

Model Selection

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