activity
20182022
most citedA deep network for sinogram and CT image reconstruction

8 citations · 27 across the 11 of their papers we have counts for

collaborators

14 papers

cs.HC2022

Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles

Feng Zhou, Tao Chen, Baiying Lei

There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major cha…

cs.LG20213 cited

A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease

Qiankun Zuo, Baiying Lei, Shuqiang Wang +3

Alzheimer's disease is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnost…

cs.LG20214 cited

DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease

Junren Pan, Baiying Lei, Shuqiang Wang +3

One of the main reasons for Alzheimer's disease (AD) is the disorder of some neural circuits. Existing methods for AD prediction have achieved great success, however, detecting abn…

cs.CV20211 cited

Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis

Junren Pan, Baiying Lei, Yanyan Shen +3

Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. Over recent years the neuroimaging communi…

cs.CV20214 cited

Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction

Qiankun Zuo, Baiying Lei, Yanyan Shen +3

Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover,…

eess.IV20212 cited

A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction

Bowen Hu, Baiying Lei, Yanyan Shen +2

Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accu…