Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
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.
IEEE

Inconsistency-Aware Graph Convolutional Networks for Multi-View Classification

Abstract: Graph convolutional networks (GCNs) have attracted significant attention in the field of multi-view learning, as they effectively extract intricate information from diverse features.
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 ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...