1 code implementation • 28 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.
no code implementations • 9 Dec 2024 • Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, Zhongkai Xue, Lichao Sun, Lifang He, Hanjie Chen, Kaize Ding, Zijian Du, Fangzhou Mu, Jiaxin Pei, Jieyu Zhao, Swabha Swayamdipta, Willie Neiswanger, Hua Wei, Xiyang Hu, Shixiang Zhu, Tianlong Chen, Yingzhou Lu, Yang Shi, Lianhui Qin, Tianfan Fu, Zhengzhong Tu, Yuzhe Yang, Jaemin Yoo, Jiaheng Zhang, Ryan Rossi, Liang Zhan, Liang Zhao, Emilio Ferrara, Yan Liu, Furong Huang, Xiangliang Zhang, Lawrence Rothenberg, Shuiwang Ji, Philip S. Yu, Yue Zhao, Yushun Dong
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection.
no code implementations • 9 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.
no code implementations • 13 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.
1 code implementation • 25 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.
1 code implementation • 24 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.
no code implementations • 23 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.
no code implementations • 21 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.
no code implementations • 30 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.
1 code implementation • 1 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.
no code implementations • 5 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.
1 code implementation • 3 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.
no code implementations • 25 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.
1 code implementation • 23 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.
no code implementations • 5 Jul 2022 • Carlo Amodeo, Igor Fortel, Olusola Ajilore, Liang Zhan, Alex Leow, Theja Tulabandhula
Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain.
no code implementations • 6 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.
1 code implementation • 17 Mar 2022 • Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
no code implementations • 9 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.
no code implementations • 9 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.
1 code implementation • 31 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.
no code implementations • 10 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.
no code implementations • 19 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.
no code implementations • 23 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.
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.
no code implementations • 19 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.