Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
Frontiers

RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...
IEEE

A Novel Low-resource Graph Convolutional Neural Network Approach for Entity Recognition

Abstract: With the rise of large language models in natural language processing, the use of various generative large language models for entity recognition has become a cutting-edge research approach.
Scientific Research Publishing

Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P., et al. (2017) Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network. IEEE Transactions on ...

ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...
IEEE

MAFD-GCN: Multi-view Adaptive Frequency Dynamic Graph Convolutional Networks

Abstract: Link prediction serves as a fundamental task within dynamic graph convolutional networks, an area that has attracted substantial scholarly attention in recent years. However, a majority of ...
Microsoft

xGCN An Extreme Graph Convolutional Network for Large-scale Social Link Prediction

\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...