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

Showing 11 to 20 of 780 articles

Video-Based Multiphysiological Disentanglement and Remote Robust Estimation for Respiration.

IEEE transactions on neural networks and learning systems
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...

Unsupervised Domain Adaptation for Low-Dose CT Reconstruction via Bayesian Uncertainty Alignment.

IEEE transactions on neural networks and learning systems
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...

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction.

IEEE transactions on neural networks and learning systems
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...

Semi-Supervised Multimodal Representation Learning Through a Global Workspace.

IEEE transactions on neural networks and learning systems
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...

GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data.

IEEE transactions on neural networks and learning systems
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 ...

A Survey on Causal Reinforcement Learning.

IEEE transactions on neural networks and learning systems
While reinforcement learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged ...

A Novel Hierarchical Cross-Stream Aggregation Neural Network for Semantic Segmentation of 3-D Dental Surface Models.

IEEE transactions on neural networks and learning systems
Accurate teeth delineation on 3-D dental models is essential for individualized orthodontic treatment planning. Pioneering works like PointNet suggest a promising direction to conduct efficient and accurate 3-D dental model analyses in end-to-end lea...

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

IEEE transactions on neural networks and learning systems
Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art lear...

Robust Sensory Information Reconstruction and Classification With Augmented Spikes.

IEEE transactions on neural networks and learning systems
Sensory information recognition is primarily processed through the ventral and dorsal visual pathways in the primate brain visual system, which exhibits layered feature representations bearing a strong resemblance to convolutional neural networks (CN...

Pyramid Pixel Context Adaption Network for Medical Image Classification With Supervised Contrastive Learning.

IEEE transactions on neural networks and learning systems
Spatial attention (SA) mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysi...