AIMC Topic: Humans

Clear Filters Showing 17081 to 17090 of 95995 articles

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Medical & biological engineering & computing
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and sel...

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.

Medical & biological engineering & computing
The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low c...

CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.

Medical & biological engineering & computing
Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis. However, most previous studies neither effectively learn label correlations nor take full advantage...

Attitudes of nurses toward artificial intelligence: A multicenter comparison.

Work (Reading, Mass.)
BackgroundArtificial intelligence (AI) is transforming medical practices with rapidly developing technologies and the innovative solutions it provides. In order for this transformation to be successfully integrated into healthcare services, healthcar...

Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation.

Neural networks : the official journal of the International Neural Network Society
Multi-scenario and multi-task learning are crucial in industrial recommendation systems to deliver high-quality recommendations across diverse scenarios with minimal computational overhead. However, conventional models often fail to effectively lever...

Riemannian manifold-based disentangled representation learning for multi-site functional connectivity analysis.

Neural networks : the official journal of the International Neural Network Society
Functional connectivity (FC), derived from resting-state functional magnetic resonance imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC is usually defined as a correlation matrix that is a symmetric positiv...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

International journal of medical informatics
BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledg...

Illumination-Guided progressive unsupervised domain adaptation for low-light instance segmentation.

Neural networks : the official journal of the International Neural Network Society
Due to limited photons, low-light environments pose significant challenges for computer vision tasks. Unsupervised domain adaptation offers a potential solution, but struggles with domain misalignment caused by inadequate utilization of features at d...

CDCGAN: Class Distribution-aware Conditional GAN-based minority augmentation for imbalanced node classification.

Neural networks : the official journal of the International Neural Network Society
Node classification is a fundamental task of Graph Neural Networks (GNNs). However, GNN models tend to suffer from the class imbalance problem which deteriorates the representation ability of minority classes, thus leading to unappealing classificati...

Large Language Model Enhanced Logic Tensor Network for Stance Detection.

Neural networks : the official journal of the International Neural Network Society
Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining. However, traditional deep neural networks for stance detection often suffer from limita...