Neurology

Head Trauma

Latest AI and machine learning research in head trauma for healthcare professionals.

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CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better Interpretability of Intelligent Fault Diagnosis

Neural networks (NNs), with their powerful nonlinear mapping and end-to-end capabilities, are wide...

AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers

Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and ...

Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions

Individual's general well-being is greatly impacted by mental health conditions including depressi...

Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities

Mental illness is a widespread and debilitating condition with substantial societal and personal c...

Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization-Based Shepard Convolutional Neural Network.

The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve proble...

Feb 2025 39832921
Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines

Quantizing deep neural networks ,reducing the precision (bit-width) of their computations, can rem...

A Post-Processing-Based Fair Federated Learning Framework

Federated Learning (FL) allows collaborative model training among distributed parties without pool...

Post-hoc Spurious Correlation Neutralization with Single-Weight Fictitious Class Unlearning

Neural network training tends to exploit the simplest features as shortcuts to greedily minimize t...

Coded Deep Learning: Framework and Algorithm

The success of deep learning (DL) is often achieved with large models and high complexity during b...

A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understa...

Diffusion Adversarial Post-Training for One-Step Video Generation

The diffusion models are widely used for image and video generation, but their iterative generatio...

UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

The low-altitude economy (LAE) plays an indispensable role in cargo transportation, healthcare, in...

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Healthy brain networks usually show highly efficient information communication and self-sustained ...

Demystifying Domain-adaptive Post-training for Financial LLMs

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach ...

Neural xenografts contribute to long-term recovery in stroke via molecular graft-host crosstalk

Stroke is a leading cause of disability and death due to the brain’s limited ability to regenerate d...

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: a machine learning approach

Transcranial Magnetic Stimulation (TMS) with simultaneous Electroencephalogram (TMS-EEG) allows asse...

Inception: Simulating Personalized Long-Term Recovery in Disorders of Consciousness using Whole-Brain Computational Perturbations

Advancements in the treatment of Disorders of Consciousness have seen significant progress with per-...

Cognitive training effects are shaped more by individual brain dynamics than age – Evidence from younger and older women

Given the well-established structural and functional changes in the aging brain, it is widely assume...

Tonotopically distinct OFF responses arise in the mouse auditory midbrain following sideband suppression

The parsing of sensory information into discrete topographic domains is a fundamental principle of s...

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