Frontiers

DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification

Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and ...
GitHub

[ICLR 2026] VeloxSeg: Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

Overview of VeloxSeg. VeloxSeg employs an encoder-decoder architecture with Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC) on the left, using 1x1 convolution as ...
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 ...
Scientific Research Publishing

Dual-Dilated Large Kernel Convolution for Visual Attention Network ()

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...
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

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 ...
News Medical

New AI model performs medical image segmentation with far less data

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...