Neurology

Federated Learning via Conditional Mutual Learning for Alzheimer's Disease Classification on T1w MRI.

Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privacy constraints lead to the fact that existing medical datasets are small-scale and distributed. Federated learning via model distillation is a data-pri...

Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer's Disease Patients.

Mild Cognitive Impairment (MCI) is the stage between the declining of normal brain function and the more serious decline of dementia. Alzheimer's disease (AD) is one of the leading forms of dementia. Although MCI does not always lead to AD, an early diagnosis of MCI may be helpful in finding those with early signs of AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has utilized magnetic ...

Classification of Epileptic Seizure From EEG Signal Based on Hilbert Vibration Decomposition and Deep Learning.

A convolution neural network (CNN) architecture has been designed to classify epileptic seizures based on two-dimensional (2D) images constructed from...

Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images.

Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly ...

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it ...

A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection.

Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features a...

Seizure Type Classification Using EEG Based on Gramian Angular Field Transformation and Deep Learning.

classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while mos...

DeepQSMSeg: A Deep Learning-based Sub-cortical Nucleus Segmentation Tool for Quantitative Susceptibility Mapping.

Deep brain nuclei are closely related to the pathogenesis of neurodegenerative diseases. Automatic segmentation for brain nuclei plays a significant r...

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This ca...

Towards Stroke Biomarkers on Fundus Retinal Imaging: A Comparison Between Vasculature Embeddings and General Purpose Convolutional Neural Networks.

Fundus Retinal imaging is an easy-to-acquire modality typically used for monitoring eye health. Current evidence indicates that the retina, and its va...

Gaze-controlled Robot-assisted Painting in Virtual Reality for Upper-limb Rehabilitation.

Stroke is the leading cause of adult disability. Robot-assisted rehabilitation systems show great promise for motor recovery after a stroke. In this w...

A Virtual Scanning Framework for Robotic Spinal Sonography with Automatic Real-time Recognition of Standard Views.

Ultrasound (US) imaging is widely used to assist in the diagnosis and intervention of the spine, but the manual scanning process would bring heavy phy...

A Multimodal Direct Gaze Interface for Wheelchairs and Teleoperated Robots.

Gaze-based interfaces are especially useful for people with disabilities involving the upper limbs or hands. Typically, users select from a number of ...

Assistive Sliding Mode Control of a Rehabilitation Robot with Automatic Weight Adjustment.

There are approximately 13 million new stroke cases worldwide each year. Research has shown that robotics can provide practical and efficient solution...

EEG-Based Emotion Recognition for Modulating Social-Aware Robot Navigation.

Companion robots play an important role to accompany humans and provide emotional support, such as reducing human social isolation and loneliness. Bas...

Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an impo...

Differentiating Motor Coordination in Children with Cerebral Palsy and Typically Developing Populations Through Exploratory Factor Analysis of Robotic Assessments.

General motor and executive functions are integral for tasks of daily living and are typically assessed when quantifying impairment of an individual. ...

EEG-based Emotion Recognition Using Graph Convolutional Network with Learnable Electrode Relations.

Emotion recognition based on electroencephalography (EEG) plays a pivotal role in the field of affective computing, and graph convolutional neural net...

Reduction of the ERP Measurement Time by a Weighted Averaging Using Deep Learning.

In clinical examination, event-related potentials (ERPs) are estimated by averaging across multiple responses, which suppresses background EEG. Howeve...

Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips.

Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wear...