AI Medical Compendium Journal:
Neuroinformatics

Showing 11 to 20 of 85 articles

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models.

Neuroinformatics
Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalograp...

AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning.

Neuroinformatics
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the ...

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

Neuroinformatics
Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to captu...

Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease.

Neuroinformatics
Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing lit...

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Neuroinformatics
Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in...

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images.

Neuroinformatics
Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhance...

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.

Neuroinformatics
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble metho...

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Neuroinformatics
Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a beneficial diagnostic tool in treatment planning. Treatment response assessment in g...

Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.

Neuroinformatics
Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly ...

Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives.

Neuroinformatics
Deep learning algorithms have a huge influence on tackling research issues in the field of medical image processing. It acts as a vital aid for the radiologists in producing accurate results toward effective disease diagnosis. The objective of this r...