Frontiers

A multi-domain graph convolutional network-based prediction model for personalized motor imagery action

Brain-computer interfaces (BCIs) are advanced and innovative systems that enable direct communication between humans and external devices by utilizing data encoded in the brain activity (Shi et al., ...
IEEE

DC-GCN: A Lightweight Graph Convolutional Network Integrating Local and Global Context for Semantic Segmentation

Abstract: Graph convolutional networks (GCNs) have demonstrated remarkable performance in hyperspectral image classification and remote sensing scene recognition. However, their application to ...
Nature

Image segmentation network based on enhanced dual encoder

Medical image segmentation plays a crucial role in image analysis, focusing on the identification and delineation of specific structures or areas of interest within medical images. By accurately ...
IEEE

BGC-Net: Bilateral Graph Convolutional Network for Weakly Supervised Semantic Segmentation of Large-Scale Point Clouds

Abstract: Weakly-supervised point cloud semantic segmentation (WS-PCS) has attracted increasing attention due to the challenge of sparse annotations. A central problem is how to effectively extract ...
Nature

Graph neural network model using radiomics for lung CT image segmentation

Early detection of lung cancer is critical for improving treatment outcomes, and automatic lung image segmentation plays a key role in diagnosing lung-related diseases such as cancer, COVID-19, and ...