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

Showing 121 to 130 of 780 articles

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

IEEE transactions on neural networks and learning systems
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary charact...

Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust.

IEEE transactions on neural networks and learning systems
The first step toward investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control g...

SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar...

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

IEEE transactions on neural networks and learning systems
The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphologic...

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

IEEE transactions on neural networks and learning systems
The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. T...

Holistic-Guided Disentangled Learning With Cross-Video Semantics Mining for Concurrent First-Person and Third-Person Activity Recognition.

IEEE transactions on neural networks and learning systems
The popularity of wearable devices has increased the demands for the research on first-person activity recognition. However, most of the current first-person activity datasets are built based on the assumption that only the human-object interaction (...

Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection.

IEEE transactions on neural networks and learning systems
Computer vision syndrome causes vision problems and discomfort mainly due to dry eye. Several studies show that dry eye in computer users is caused by a reduction in the blink rate and an increase in the prevalence of incomplete blinks. In this conte...

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

IEEE transactions on neural networks and learning systems
Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The...

N-Level Hierarchy-Based Optimal Control to Develop Therapeutic Strategies for Ecological Evolutionary Dynamics Systems.

IEEE transactions on neural networks and learning systems
This article mainly proposes an evolutionary algorithm and its first application to develop therapeutic strategies for ecological evolutionary dynamics systems (EEDS), obtaining the balance between tumor cells and immune cells by rationally arranging...

Hierarchical Context-Based Emotion Recognition With Scene Graphs.

IEEE transactions on neural networks and learning systems
For a better intention inference, we often try to figure out the emotional states of other people in social communications. Many studies on affective computing have been carried out to infer emotions through perceiving human states, i.e., facial expr...