Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas from MRI

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurosurgery, Medical Imaging, Oncology,
gliomas   IDH prediction   multi-level features   MRI   feature fusion  

Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in MRI Background Glioma is the most common malignant primary brain tumor in adults. According to the 2021 World Health Organization (WHO) classification of tumors, genotype plays a significant role in the classification of tumor subtypes, especially the isocitrate dehy...

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Medical Imaging, Oncology,
normalizing flow   pharmacokinetic parameters   dynamic contrast-enhanced MRI   maximum likelihood estimation   posterior distribution  

In modern medical diagnostics and clinical research, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) technology provides significant information regarding tissue pathophysiology. By fitting a Tracer-Kinetic (TK) model, pharmacokinetic (PK) parameters can be extracted from time-series MRI signals. However, these estimated PK parameter...

A Siamese-Transport Domain Adaptation Framework for 3D MRI Classification of Gliomas and Alzheimer’s Diseases

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Medical Imaging, Oncology,
3D MRI   Gliomas   Alzheimer's Disease   Domain Adaptation   Deep Learning  

Classification of 3D MRI Gliomas and Alzheimer’s Disease Based on the Siamese-Transport Domain Adaptation Framework Background In computer-aided diagnosis, 3D magnetic resonance imaging (MRI) screening plays a vital role in the early diagnosis of various brain diseases, effectively preventing the deterioration of the condition. Glioma is a common m...

Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Oncology,
brain tumor classification   deep learning   ensemble learning   multiclass classification   transfer learning   explainable AI   vision transformers  

In recent years, due to the high incidence and lethality of brain tumors, rapid and accurate detection and classification of brain tumors have become particularly important. Brain tumors include both malignant and non-malignant types, and their abnormal growth can cause long-term damage to the brain. Magnetic Resonance Imaging (MRI) is a commonly u...

Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma after Tumor Resection

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Oncology,
brain   glioma   growth model   magnetic resonance imaging   validation  

Research Review on the Predictive Value of Low-Grade Glioma Postoperative Growth Models Introduction Glioma is an aggressive brain tumor whose cells rapidly diffuse within the brain. Understanding and predicting the pattern and speed of this diffusion can help optimize treatment plans. Glioma growth models based on diffusion-proliferation have show...

Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

Engineering, Electrical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence, Medicine, Orthopedics, Medical Laboratory Science, Medical Imaging, Life Sciences, Bioengineering,
physics-informed   deep learning   musculoskeletal modeling   EMG   muscle forces   joint kinematics  

Musculoskeletal models have been widely used in biomechanical analysis because they can estimate motion variables that are difficult to measure directly in living organisms, such as muscle forces and joint moments. Traditional physics-driven computational musculoskeletal models can explain the dynamic interactions between neural inputs to muscles, ...

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke through EMG-Driven Robot Hand Training: Insights from Dynamic Causal Modeling

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke through EMG-Driven Robot Hand Training: Insights from Dynamic Causal Modeling

Engineering, Electrical Engineering, Information Science, Automation, Medicine, Basic Medical Sciences, Neurology, Medical Imaging, Life Sciences, Bioengineering,
EMG-driven   robot hand training   stroke rehabilitation   neural networks   dynamic causal modeling   upper limb function recovery  

Revealing the Neuromechanism of Interhemispheric Balance Restoration in Chronic Stroke Patients through EMG-driven Robot Hand Training: Insights from Dynamic Causal Modeling Stroke is a common cause of disability, with most stroke survivors suffering from upper limb paralysis. The consequences of upper limb functional impairment can persist for ove...

Wavelet-Based Temporal-Spectral-Attention Correlation Coefficient for Motor Imagery EEG Classification

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Basic Medical Sciences, Neurology, Medical Imaging, Life Sciences, Bioengineering,
brain-computer interface   motor imagery   EEG channel attention   temporal-spectral attention   wavelet transform   correlation coefficient  

Brain-Computer Interface (BCI) technology has rapidly developed in recent years and is considered a cutting-edge technology that allows external devices to be controlled directly by the brain without the need for peripheral nerves and muscles. Particularly in the application of Motor Imagery Electroencephalography (MI-EEG), BCI technology has shown...

Magnetoencephalography-Derived Oscillatory Microstate Patterns Across Lifespan: The Cambridge Centre for Ageing and Neuroscience Cohort

Medicine, Psychiatry, Geriatrics, Neurology, Medical Imaging, Life Sciences, Biophysics,
machine learning   microstates   healthy aging   alpha  

Application of Magnetoencephalography (MEG) to Analyze Changes in Whole-Brain Oscillatory Microstate Patterns Across the Lifespan: Cambridge Centre for Aging and Neuroscience Cohort Study Research Background With the increasing seriousness of the aging population problem, understanding the neurophysiological changes during the aging process becomes...

The Cortical Neurophysiological Signature of Amyotrophic Lateral Sclerosis

Medicine, Neurology, Medical Imaging, Life Sciences, Immunology, Biophysics,
Amyotrophic Lateral Sclerosis   Motor Neuron Disease   GABAergic Interneuronal Circuits   Frequency Band Analysis   Electromagnetic Neuroimaging  

Analysis of Cortical Neurophysiological Characteristics of ALS and Its Potential as a Biomarker Background Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that affects adults, characterized by a gradual loss of the integrity of the brain, spinal cord, and peripheral motor system. Although clinical and genetic studies have reveale...