31 citations · 109 across the 15 of their papers we have counts for
21 papers
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
Yao Su, Zhentian Qian, Lifang He +1
Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (i.e., extraction s…
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
Yao Su, Xin Dai, Lifang He +1
Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neu…
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Xuetong Wang, Kanhao Zhao, Rong Zhou +4
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modelin…
Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis
Jun Yu, Zhaoming Kong, Liang Zhan +2
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combi…
Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis
Houliang Zhou, Lifang He, Yu Zhang +2
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpreta…
Task Modifiers for HTN Planning and Acting
Weihang Yuan, Hector Munoz-Avila, Venkatsampath Raja Gogineni +3
The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments. In order to provide this capability to hierarchical task net…