138 citations · 317 across the 10 of their papers we have counts for
10 papers
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
Heng Li, Haofeng Liu, Huazhu Fu +5
Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation…
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
Yuchen Dang, Ziqi Chen, Heng Li +1
Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important n…
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation
Qiran Jia, Hai Shu
Convolutional neural networks (CNNs) have achieved remarkable success in automatically segmenting organs or lesions on 3D medical images. Recently, vision transformer networks have…
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
Chenggang Lyu, Hai Shu
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder b…
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…
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…
CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets
Hai Shu, Zhe Qu
A representative model in integrative analysis of two high-dimensional correlated datasets is to decompose each data matrix into a low-rank common matrix generated by latent factor…
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…
Estimation of Large Covariance and Precision Matrices from Temporally Dependent Observations
Hai Shu, Bin Nan
We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. Th…
Multiple Testing for Neuroimaging via Hidden Markov Random Field
Hai Shu, Bin Nan, Robert Koeppe
Traditional voxel-level multiple testing procedures in neuroimaging, mostly -value based, often ignore the spatial correlations among neighboring voxels and thus suffer from sub…