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This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. […] Read more

The hierarchical organization of the visual system results in topology, which often gets lost in the “raw” human retinotopic maps derived from BOLD fMRI recordings.  […]  Read more

Diffeomorphic Registration for Retinotopic Maps of Multiple Visual Regions

Diffeomorphic Registration for Retinotopic Maps of Multiple Visual Regions […]  Read more

ISBI2022_Jianfeng

Investigating the Effect of Tau Deposition and APOE on Hippocampal […]  Read more

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Federated Morphometry Feature Selection for Hippocampal Morphometry […]  Read more

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Federated Morphometry Feature Selection for Hippocampal Morphometry […]  Read more

retinotopic map

Specific areas of the human brain process different functions, such as the […]  Read more

Geometry-Aware Hierarchical Bayesian Learning on Manifolds

Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan, […]  Read more

Quantitative Characterization of the Human Retinotopic MapBased on Quasiconformal Mapping

Quantitative Characterization of the Human Retinotopic MapBased on […]  Read more

Predicting Brain Amyloid using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals from the ADNI and OASIS Databases

Predicting Brain Amyloid using Multivariate Morphometry Statistics, Sparse […]  Read more

MICCAI2021

Yanshuai received the Student Travel Awards for MICCAI 2021, the 24th […]  Read more

Tetrahedral Spectral Feature-Based Bayesian Manifold Learning for Grey Matter Morphometry: Findings from the Alzheimer’s DiseaseNeuroimaging Initiative

Tetrahedral Spectral Feature-Based Bayesian Manifold Learning for Grey […]  Read more

Topological Receptive Field Model for Human Retinotopic Mapping

Topological Receptive Field Model for Human Retinotopic Mapping Yanshuai […]  Read more

Well done! Congratulations, Jie! We wish you all the best! […]  Read more

Integrating Multimodal and Longitudinal Neuroimaging Data with […]  Read more

OHBM 2021

Jianfeng Wu received Merit Abstract Award for the 2021 OHBM Annual […]  Read more

VSS 2022

Duyan and Yanshuai both received the Travel Awards for Annual Meeting of […]  Read more

Developing Univariate Neurodegeneration Biomarkers with Low-Rank and […]  Read more

Applying Surface-Based Morphometry to Study Ventricular Abnormalities of […]  Read more

Deep Representation Learning For Multimodal Brain Networks

Deep Representation Learning For Multimodal Brain Networks Wen Zhang, […]  Read more

Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in Retinotopic Mapping

Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller […]  Read more

Integrating Convolutional Neural Networks and Multi-task Dictionary […]  Read more

COMPUTING UNIVARIATE NEURODEGENERATIVE BIOMARKERS WITH VOLUMETRIC OPTIMAL […]  Read more

IEEE ISBI 2020

Congratulations on that 5 papers were accepted from our group in ISBI […]  Read more

ASU Graduate College will offer Wen Zhang the prestigious Graduate College Completion Fellowship for Spring 2020. Congratulations!

ASU Graduate College will offer Wen Zhang the prestigious Graduate College […]  Read more

Regularized Wasserstein Means for Aligning Distributional Data

Regularized Wasserstein Means for Aligning Distributional Data Liang Mi, […]  Read more

Continually Modeling Alzheimer's Disease Progression via Deep Multi-Order Preserving Weight Consolidation

Continually Modeling Alzheimer’s Disease Progression via Deep Multi-Order […]  Read more

ASU Graduate College will offer Liang Mi a Graduate College Completion Fellowship for the Fall 2019 and Spring 2020 academic terms. Congratulations!

ASU Graduate College will offer Liang Mi a Graduate College Completion […]  Read more

APPLYING SURFACE-BASED HIPPOCAMPAL MORPHOMETRY TO STUDY APOE-E4 ALLELE DOSE EFFECTS IN COGNITIVELY UNIMPAIRED SUBJECTS

APPLYING SURFACE-BASED HIPPOCAMPAL MORPHOMETRY TO STUDY APOE-E4 ALLELE […]  Read more

A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data

A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain […]  Read more

This work studies the Wasserstein space and proposes a novel framework to compute the Wasserstein distance between general topological surfaces by integrating hyperbolic Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram algorithms. The resulting hyperbolic Wasserstein distance can intrinsically measure the similarity between general topological surfaces.

Jie Shi, Yalin Wang Accepted by IEEE Transactions on Pattern Analysis and […]  Read more

We propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature – IPF) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure.

Liqun Kuang, Xie Han, Kewei Chen, Richard J. Caselli, Eric M. Reiman, […]  Read more

A new clustering method which solves the k-means clustering problem with variational optimal transportation. It leverages power Voronoi diagram to aggregate empirical observations into a fixed number of Voronoi cells while maintaining the minimum transportation cost and preliminary results have shown its applications in domain adaptation, remeshing, and representation learning.

A new clustering method which solves the k-means clustering problem with […]  Read more

We use tetrahedral mesh as a volumetric modeling method for analyzing the cerebral cortex thickness morphometry. A discriminative heat flux signature (tHFS) and an affiliated diffeomorphic metric, the tHFS distance, are proposed. The new thickness descriptor performs significantly better in disease severity classification experiments.

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang We use tetrahedral mesh […]  Read more

In the paper, we propose a general coupling framework, the multimodal neuroimaging network fusion with longitudinal couplings (MMLC), to learn the latent representations of brain networks. The new approach integrates information from longitudinal, multimodal neuroimaging data and boosts statistical power to predict psychometric evaluation measures.

Wen Zhang, Kai Shu, Suhang Wang, Huan Liu, Yalin Wang In the paper, we […]  Read more

Conformal Geometry: Computational Algorithms and Engineering Applications

Conformal Geometry: Computational Algorithms and Engineering Applications […]  Read more

We introduce a novel Recurrent neural networks (RNNs) model compression approach DirNet based on an optimized fast dictionary learning algorithm, which 1) dynamically mines the dictionary atoms of the projection dictionary matrix within layer to adjust the compression rate 2) adaptively changes the sparsity of sparse codes cross the hierarchical layers. Experimental results on language model and an ASR model trained with a 1000h speech dataset demonstrate that our method reduces the size of original model by eight times with real-time model inference and negligible accuracy loss.

Jie Zhang, Xiaolong Wang, Dawei Li, Yalin Wang IJCAI-ECAI-2018 (Oral) We […]  Read more

We proposed a novel isometry invariant shape descriptor, based on area-preserving mapping, for brain morphometry analysis to help assess Alzheimer's Disease influence on cortical surface. Our method out-performs the Spherical Harmonic, weighted Spherical Harmonic, and Freesurfer thickness based methods on a dataset with beta-amyloid plaques confirmed subjects.

Yanshuai Tu, Chengfeng Wen, Wen Zhang, Jianfeng Wu, Jie Zhang, Kewei Chen, […]  Read more

Abstract: Cortical thickness estimation performed via magnetic resonance imaging (MRI) is an effective measure of brain atrophy in preclinical individuals at high risk for Alzheimer's disease (AD). However, the high dimensionality of individual cortical thickness data coupled with small population samples make it challenging to perform cortical thickness feature selection for AD diagnosis and prognosis. Thus far, there are very few methods that can accurately predict future clinical scores using longitudinal cortical thickness measures. In this paper, we propose an unsupervised dictionary learning algorithm, termed Multi-task Sparse Screening (MSS) that produces improved results over previous methods within this problem domain. Specifically, we formulate and solve a multi-task proablem using extracted top-significant features from the ADNI longitudinal data. Empirical studies on publicly available longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset () demonstrate improved correlation coefficients and root mean square errors, when compared to other algorithms.

Jie Zhang*, Yanshuai Tu, Qingyang Li, Richard Caselli, Paul Thompson, […]  Read more

Abstract: Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques Aβ in human brains. Most of prior brain imaging researchers used the clinical symptom based diagnosis without the confirmation of imaging or fluid Aβ information. In this work, we study hippocampus morphometry on a cohort consisting of Aβ positive AD (N=151) and matched Aβ negative cognitively unimpaired subjects (N=271) with Aβ positivity determined via florbetapir PET. The brain images are obtained from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI). We compute our surface multivariate morphometry statistics from segmented hippocampus structure in structural MR images. With these features, we find statistically significant difference by using Hotelling's tests. Meanwhile, we apply a patch-based analysis of sparse coding system for binary group classification and achieve an accuracy rate of 90.48%. Our results demonstrate that our surface multivariate morphometry statistics (MMS) perform better than traditional hippocampal volume measures in classification and it may be applied as a potential biomarker for distinguishing dementia due to AD from age matched normal aging individuals.

Jianfeng Wu, Jie Zhang, Jie Shi, Kewei Chen, Richard Caselli, Eric Reiman, […]  Read more

Congratulate on Dr. Yalin Wang’s elevation to the grade of IEEE Senior […]  Read more

An Optimal Transportation based Univariate Neuroimaging Index

An Optimal Transportation based Univariate Neuroimaging Index Mi L, Zhang […]  Read more

Yalin Wang

Dr. Yalin Wang has been promoted from assistant professor to associate […]  Read more

Congratulations to Dr. Yalin Wang, director of Geometry Systems Laboratory and a faculty of CIDSE at ASU, on his

Congratulations to Dr. Yalin Wang, director of Geometry Systems Laboratory […]  Read more

IPMI 2017

Congratulations on Jie Zhang’s Junior Scientists Scholarship in IPMI 2017! […]  Read more

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive […]  Read more

Empowering Cortical Thickness Measures in Clinical Diagnosis of Alzheimer's Disease with Spherical Sparse Coding

Empowering Cortical Thickness Measures in Clinical Diagnosis of […]  Read more

Enhancing Diffusion MRI Measures By Integrating Grey And White Matter Morphometry With Hyperbolic Wasserstein Distance

Enhancing Diffusion MRI Measures By Integrating Grey And White Matter […]  Read more

new Website

The new website is finally online! Hope it will privide a good […]  Read more