AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Attention

Showing 131 to 140 of 554 articles

Clear Filters

Spiking generative adversarial network with attention scoring decoding.

Neural networks : the official journal of the International Neural Network Society
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural n...

A syntactic evidence network model for fact verification.

Neural networks : the official journal of the International Neural Network Society
In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use t...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

Journal of affective disorders
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presenta...

Seizure Detection Based on Lightweight Inverted Residual Attention Network.

International journal of neural systems
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detectio...

Multiscale knowledge distillation with attention based fusion for robust human activity recognition.

Scientific reports
Knowledge distillation is an effective approach for training robust multi-modal machine learning models when synchronous multimodal data are unavailable. However, traditional knowledge distillation techniques have limitations in comprehensively trans...

EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.

Sensors (Basel, Switzerland)
Human emotions are complex psychological and physiological responses to external stimuli. Correctly identifying and providing feedback on emotions is an important goal in human-computer interaction research. Compared to facial expressions, speech, or...

An automated approach for predicting HAMD-17 scores via divergent selective focused multi-heads self-attention network.

Brain research bulletin
This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to automatically predict Hamilton Depression Rating Scale-17 (HAMD-17) scores in patients with depression. ...

Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data.

Accident; analysis and prevention
Early warning of driving risks can effectively prevent collisions. However, numerous studies that predicted driving risks have suffered from the use of single data sources, insufficiently advanced models, and lack of time window analysis. To address ...

A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion.

Neural networks : the official journal of the International Neural Network Society
In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliab...

Understanding Human Cognition Through Computational Modeling.

Topics in cognitive science
One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behav...