IEEE

TGMN: Two-Stage Graph Convolutional Mamba Network for Hyperspectral Image Classification

Abstract: Local spectral features and global spatial context are essential for hyperspectral image (HSI) classification. However, existing methods based on convolutional neural networks (CNNs), graph ...
The American Journal of Managed Care

Convolutional Neural Network Improves MS Lesion Segmentation

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, making it more applicable in clinical settings. The algorithm outperformed ...
Frontiers

HHBSNet: a global channel–spatial attention and multi-scale dilated convolution network for automatic melasma segmentation

Objective: Melasma is a common acquired facial hyperpigmentation disorder characterized by symmetrical brown patches, often occurring in the zygomatic region, forehead, and upper lip. Its blurred ...
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 ...
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 ...
C&EN

Prediction of the Association between Transfer RNA and Diseases: A Deep Learning Approach Combining Multi-View Graph Convolution and Attention Mechanisms

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