Search Results for author: Liang Zhan

Found 25 papers, 9 papers with code

Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?

1 code implementation28 Jan 2025 Keqi Han, Yao Su, Lifang He, Liang Zhan, Sergey Plis, Vince Calhoun, Carl Yang

Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders.

Deep Learning Graph Attention

A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases

no code implementations9 Dec 2024 Zhepeng Wang, Runxue Bao, Yawen Wu, Guodong Liu, Lei Yang, Liang Zhan, Feng Zheng, Weiwen Jiang, yanfu Zhang

Our approach conceptualizes domain knowledge as natural language and introduces a specialized multimodal GNN capable of leveraging this uncurated knowledge to guide the learning process of the GNN, such that it can improve the model performance and strengthen the interpretability of the predictions.

Graph Representation Learning

A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis

no code implementations13 Nov 2024 Feiyu Yin, Yu Lei, Siyuan Dai, Wenwen Zeng, Guoqing Wu, Liang Zhan, Jinhua Yu

To address this issue, we propose a novel method that integrates functional and structural connectivity based on heterogeneous graph neural networks (HGNNs) to better leverage the rich heterogeneity in dual-modal images.

Data Augmentation Graph Neural Network

X-ray Made Simple: Radiology Report Generation and Evaluation with Layman's Terms

1 code implementation25 Jun 2024 Kun Zhao, Chenghao Xiao, Chen Tang, Bohao Yang, Kai Ye, Noura Al Moubayed, Liang Zhan, Chenghua Lin

Last, we show that training on the layman's terms dataset encourages models to focus on the semantics of the reports, as opposed to overfitting to learning the report templates.

SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation

1 code implementation24 May 2024 Kun Zhao, Bohao Yang, Chen Tang, Chenghua Lin, Liang Zhan

Our approach introduces several techniques: (1) Contrastive learning to differentiate between robust and non-robust response embeddings; (2) A novel metric for semantic sensitivity that combines embedding cosine distances with similarity learned through neural networks, and (3) a strategy for incorporating the evaluation results from both the SLM and LLMs.

Contrastive Learning Dialogue Evaluation

Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization

no code implementations23 May 2024 Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul Thompson, Jiayu Zhou

In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data.

Diversity

Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation

no code implementations21 May 2024 Haoteng Tang, Guodong Liu, Siyuan Dai, Kai Ye, Kun Zhao, Wenlu Wang, Carl Yang, Lifang He, Alex Leow, Paul Thompson, Heng Huang, Liang Zhan

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes.

Graph Learning

BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations

no code implementations30 Apr 2024 Kaiqiao Han, Yi Yang, Zijie Huang, Xuan Kan, Yang Yang, Ying Guo, Lifang He, Liang Zhan, Yizhou Sun, Wei Wang, Carl Yang

Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes.

Irregular Time Series Missing Values +1

Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation

1 code implementation1 Apr 2024 Bohao Yang, Kun Zhao, Chen Tang, Dong Liu, Liang Zhan, Chenghua Lin

Trainable evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with a given context.

Abstract Meaning Representation Dialogue Evaluation +2

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 Prediction

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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