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20202025
most citedCross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease

6 citations · 34 across the 18 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

eess.IV2021

Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN

Wen Yu, Baiying Lei, Yanyan Shen +5

The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for the early stag…

cs.LG2021★ 3 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.LG2021★ 4 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.CV2021★ 1 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.CV2021★ 4 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.IV2021★ 2 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…