Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Neural networks have become the standard approach for tasks such as computer vision, machine translation and pattern recognition. While they exhibit significant feature representation capabilities, they often lack interpretability. This suggests that...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
The ℓ-norm is playing an increasingly important role in unsupervised feature selection. However, existing algorithm for optimization problem with ℓ-norm constraint has two problems: First, they cannot automatically determine the sparsity, also known ...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Subgraph federated learning (subgraph-FL) is a distributed machine learning paradigm enabling cross-client collaborative training of graph neural networks (GNNs). However, real-world subgraph-FL scenarios often face subgraph heterogeneity problem, i....
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
A key challenge in Nearshore Infrared Video Super-resolution (NIVSR) is the limited high-frequency foreground information. The most common approach is to fuse frames in order to learn cross-temporal information. However, existing methods struggle to ...
Magnetic resonance imaging (MRI) has the potential to identify post-operative risk factors for re-tearing an anterior cruciate ligament (ACL) using a combination of imaging signal intensity (SI) and cross-sectional area measurements of the healing AC...
Neural networks : the official journal of the International Neural Network Society
Sep 1, 2025
Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primaril...
PURPOSE: To combine ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging with a self-attention convolutional neural network (SAT-Net) for accelerated mapping of macromolecular fraction (MMF) in cortical bone.
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...
Machine-learning algorithms are transforming healthcare diagnostics and prognostics. However, they sometimes underperform for groups underrepresented in their training data. Vandersluis and Savulescu have suggested selectively deploying these algorit...
IEEE transactions on visualization and computer graphics
Sep 1, 2025
Accurately determining the clinical positions for each tooth is essential in orthodontics, while most existing solutions heavily rely on inefficient manual design. In this paper, we present the LETA, a dual-branch Latent Encoding based 3D Tooth Align...
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