131 citations · 264 across the 8 of their papers we have counts for
8 papers
Statistically controllable microstructure reconstruction framework for heterogeneous materials using sliced-Wasserstein metric and neural networks
Zhenchuan Ma, Qizhi Teng, Pengcheng Yan +4
Heterogeneous porous materials play a crucial role in various engineering systems. Microstructure characterization and reconstruction provide effective means for modeling these mat…
Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images
Yanteng Zhang, Qizhi Teng, Xiaohai He +4
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain re…
Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
Yanteng Zhanga, Xiaohai He, Yi Hao Chan +2
In recent years, deep learning models have been applied to neuroimaging data for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and posit…
A fast and flexible algorithm for microstructure reconstruction combining simulated annealing and deep learning
Zhenchuan Ma, Xiaohai He, Pengcheng Yan +2
The microstructure analyses of porous media have considerable research value for the study of macroscopic properties. As the premise of conducting these analyses, the accurate reco…
Multiscale reconstruction of porous media based on multiple dictionaries learning
Pengcheng Yan, Qizhi Teng, Xiaohai He +2
Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characte…
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
Junxi Feng, Xiaohai He, Qizhi Teng +3
Porous media are ubiquitous in both nature and engineering applications, thus their modelling and understanding is of vital importance. In contrast to direct acquisition of three-d…
CT-image Super Resolution Using 3D Convolutional Neural Network
Yukai Wang, Qizhi Teng, Xiaohai He +2
Computed Tomography (CT) imaging technique is widely used in geological exploration, medical diagnosis and other fields. In practice, however, the resolution of CT image is usually…
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
Honggang Chen, Xiaohai He, Chao Ren +2
In recent years, much research has been conducted on image super-resolution (SR). To the best of our knowledge, however, few SR methods were concerned with compressed images. The S…