AI Medical Compendium Journal:
IEEE transactions on neural networks and learning systems

Showing 41 to 50 of 780 articles

Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN research, artificial SNNs still lack many capabilities of their biological counterparts. In biologi...

Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

IEEE transactions on neural networks and learning systems
In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, dee...

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

IEEE transactions on neural networks and learning systems
Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary nodules is essential to slow down the degenerative process and reduce mort...

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.

IEEE transactions on neural networks and learning systems
As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaboration correlations as the brain region gene community network (BG-CN) and present a new deep learning a...

Dynamic Subcluster-Aware Network for Few-Shot Skin Disease Classification.

IEEE transactions on neural networks and learning systems
This article addresses the problem of few-shot skin disease classification by introducing a novel approach called the subcluster-aware network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SC...

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

IEEE transactions on neural networks and learning systems
Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific...

Adaptive Gait Feature Learning Using Mixed Gait Sequence.

IEEE transactions on neural networks and learning systems
Gait recognition has become a mainstream technology for identification, as it can recognize the identity of subjects from a distance without any cooperation. However, when subjects wear coats (CL) or backpacks (BG), their gait silhouette will be occl...

Contrastive Registration for Unsupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using deep learning supervised techniques, they assume a large and...

SemiHAR: Improving Semisupervised Human Activity Recognition via Multitask Learning.

IEEE transactions on neural networks and learning systems
Semisupervised human activity recognition (SemiHAR) has attracted attention in recent years from various domains, such as digital health and ambient intelligence. Currently, it still faces two challenges. For one thing, discriminative features may ex...

A Semantic-Aware Attention and Visual Shielding Network for Cloth-Changing Person Re-Identification.

IEEE transactions on neural networks and learning systems
Cloth-changing person re-identification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for exist...