Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network

no code implementations • 28 Sep 2023 • Qiankun Zuo, Junren Pan, Shuqiang Wang

The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner.

Disease Prediction Generative Adversarial Network

Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification

no code implementations • 29 Jun 2022 • Changwei Gong, Changhong Jing, Junren Pan, Shuqiang Wang

Functional alterations in the relevant neural circuits occur from drug addiction over a certain period.

feature selection

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

no code implementations • 20 Jun 2022 • Junren Pan, Shuqiang Wang

However, most existing methods applied in neuroimaging can not efficiently fuse the functional and structural information from multi-modal neuroimages.

Generative Adversarial Network

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

no code implementations • 12 Oct 2021 • Junren Pan, Baiying Lei, Shuqiang Wang, BingChuan Wang, Yong liu, Yanyan Shen

In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD.

Generative Adversarial Network

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

no code implementations • 21 Jul 2021 • Junren Pan, Baiying Lei, Yanyan Shen, Yong liu, Zhiguang Feng, Shuqiang Wang

Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis.

White Matter Fiber Tractography