IEEE

Multiscale Gaussian Process-Driven Graph Convolutional Neural Network for Polarimetric SAR Image Classification

Abstract: By focusing on the structure exploration and information propagation from non-Euclidean data space, graph convolutional neural network (GCN), which can extract abundant and discriminative ...
IEEE

BAN: A Boundary-Aware Network Based on KAN for Robust Hyperspectral Image Classification Against Adversarial Attacks

Abstract: Deep neural networks (DNNs) have achieved significant advancements in hyperspectral image (HSI) classification, enabling critical applications in environmental monitoring, medical imaging, ...

Foundation AI model uses MRI data to predict multiple brain disorders

Artificial intelligence (AI) systems are computational models that can learn to identify patterns in data, make accurate predictions or generate content (e.g., texts, images, videos or sound ...

AI toolkit turns microscopy images into multi-feature microstructure datasets

A research team from The Hong Kong University of Science and Technology (HKUST) has developed GrainBot, an AI-enabled toolkit that automatically extracts and quantifies multiple microstructural ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Business Insider

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification BEIJING, Feb.06, 2026––WiMi Hologram ...