IEEE transactions on neural networks and learning systems
May 2, 2025
Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images ba...
IEEE transactions on neural networks and learning systems
May 2, 2025
Remote noncontact respiratory rate estimation by facial visual information has great research significance, providing valuable priors for health monitoring, clinical diagnosis, and anti-fraud. However, existing studies suffer from disturbances in epi...
IEEE transactions on neural networks and learning systems
May 2, 2025
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning (DL) is widely used in this problem, but the performance of testing data (also known...
IEEE transactions on neural networks and learning systems
May 2, 2025
Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representation framework for describing protein functions with annotated terms. Each ontology is a specific fu...
IEEE transactions on neural networks and learning systems
May 2, 2025
Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations or to translate signals from one domain to another (as in image captioning or text-to-image g...
IEEE transactions on neural networks and learning systems
May 2, 2025
Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate development and heterogeneity. While numerous machine-learning methods have been proposed to infer GRNs ...
Neural networks : the official journal of the International Neural Network Society
May 1, 2025
Federated learning (FL) is a research area focusing on model training across numerous users while preserving data privacy under the coordination of a central server. The inherent optimization challenges in FL often manifest as nonconvex and nonsmooth...
Neural networks : the official journal of the International Neural Network Society
May 1, 2025
Binary code similarity detection (BSCD), whose goal is to identify and analyze similar or identical functions in compiled binaries, is an essential task in computer security. Recent methods leveraging deep neural networks (DNN) for numerical vector r...
PURPOSE: Conventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develo...
Neural networks : the official journal of the International Neural Network Society
May 1, 2025
Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to thei...
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