576 citations · 827 across the 28 of their papers we have counts for
29 papers
Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces
Hengchao Chen, Yuanyao Tan, Chao Huang +2
This paper introduces Mixtures of Geodesic Factor Analyzers (MGFA) on Riemannian homogeneous spaces. MGFA uses a geodesic factor model within each mixture component, providing grea…
Evaluating Large Language Models for Radiology Natural Language Processing
Zhengliang Liu, Tianyang Zhong, Yiwei Li +43
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they hav…
Source-Free Unsupervised Domain Adaptation: A Survey
Yuqi Fang, Pew-Thian Yap, Weili Lin +2
Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different doma…
Statistical learning methods for neuroimaging data analysis with applications
Hongtu Zhu, Tengfei Li, Bingxin Zhao
The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies…
A Partially Functional Linear Modeling Framework for Integrating Genetic, Imaging, and Clinical Data
Ting Li, Yang Yu, J. S. Marron +1
This paper is motivated by the joint analysis of genetic, imaging, and clinical (GIC) data collected in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a r…
Estimating trans-ancestry genetic correlation with unbalanced data resources
Bingxin Zhao, Xiaochen Yang, Hongtu Zhu
The aim of this paper is to propose a novel estimation method of using genetic-predicted observations to estimate trans-ancestry genetic correlations, which describes how genetic a…
On block-wise and reference panel-based estimators for genetic data prediction in high dimensions
Bingxin Zhao, Shurong Zheng, Hongtu Zhu
Genetic prediction of complex traits and diseases has attracted enormous attention in precision medicine, mainly because it has the potential to translate discoveries from genome-w…
High-Dimensional Spatial Quantile Function-on-Scalar Regression
Zhengwu Zhang, Xiao Wang, Linglong Kong +1
This paper develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given…
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Hai Shu, Ronghua Shi, Qiran Jia +2
Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve ad…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
Hai Shu, Zhe Qu, Hongtu Zhu
Modern biomedical studies often collect multi-view data, that is, multiple types of data measured on the same set of objects. A popular model in high-dimensional multi-view data an…
Cross-trait prediction accuracy of high-dimensional ridge-type estimators in genome-wide association studies
Bingxin Zhao, Hongtu Zhu
Marginal association summary statistics have attracted great attention in statistical genetics, mainly because the primary results of most genome-wide association studies (GWAS) ar…
Partial Least Squares for Functional Joint Models
Yue Wang, Joseph Ibrahim, Hongtu Zhu
Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJ…
ACE of Space: Estimating Genetic Components of High-Dimensional Imaging Data
Benjamin B. Risk, Hongtu Zhu
It is of great interest to quantify the contributions of genetic variation to brain structure and function, which are usually measured by high-dimensional imaging data (e.g., magne…
On genetic correlation estimation with summary statistics from genome-wide association studies
Bingxin Zhao, Hongtu Zhu
Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample siz…
Sensitivity Analysis of Deep Neural Networks
Hai Shu, Hongtu Zhu
Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is cr…
Tensor network factorizations: Relationships between brain structural connectomes and traits
Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu +1
Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. The structural connectome is initially sha…
CLT for linear spectral statistics of large dimensional sample covariance matrices with dependent data
Shurong Zheng, Zhidong Bai, Jianfeng Yao +1
This paper investigates the central limit theorem for linear spectral statistics of high dimensional sample covariance matrices of the form $\mathbf{B}_n=n^{-1}\sum_{j=1}^{n}\mathb…
A Review of Statistical Methods in Imaging Genetics
Farouk S. Nathoo, Linglong Kong, Hongtu Zhu
With the rapid growth of modern technology, many large-scale biomedical studies have been/are being/will be conducted to collect massive datasets with large volumes of multi-modali…
Bayesian spatial transformation models with applications in neuroimaging data
Michelle F. Miranda, Hongtu Zhu, Joseph G. Ibrahim
The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) v…
Local Region Sparse Learning for Image-on-Scalar Regression
Yao Chen, Xiao Wang, Linglong Kong +1
Identification of regions of interest (ROI) associated with certain disease has a great impact on public health. Imposing sparsity of pixel values and extracting active regions sim…
Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data
Zhao-Hua Lu, Sy-Miin Chow, Andrew Sherwood +1
Ambulatory cardiovascular (CV) measurements provide valuable insights into individuals' health conditions in "real-life," everyday settings. Current methods of modeling ambulatory…
Extrinsic local regression on manifold-valued data
Lizhen Lin, Brian St. Thomas, Hongtu Zhu +1
We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in…
TPRM: Tensor partition regression models with applications in imaging biomarker detection
Michelle F. Miranda, Hongtu Zhu, Joseph G. Ibrahim
Medical imaging studies have collected high dimensional imaging data to identify imaging biomarkers for diagnosis, screening, and prognosis, among many others. These imaging data a…