6 citations · 34 across the 18 of their papers we have counts for
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Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease
Junren Pan, Shuqiang Wang
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied…
Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification
Changwei Gong, Changhong Jing, Junren Pan +1
Functional alterations in the relevant neural circuits occur from drug addiction over a certain period. And these significant alterations are also revealed by analyzing fMRI. Howev…
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…
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…
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
Senrong You, Yong Liu, Baiying Lei +1
Magnetic resonance imaging plays an important role in computer-aided diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acqu…
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Shengye Hu, Baiying Lei, Yong Wang +3
Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body. But PET data is always unavailable due to diffe…
Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network
Shuqiang Wang, Zhuo Chen, Wen Yu +1
The state-of-the-art deep learning methods have demonstrated impressive performance in segmentation tasks. However, the success of these methods depends on a large amount of manual…