[HTML][HTML] Deep learning based synthesis of MRI, CT and PET: Review and analysis
Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …
aiming to overcome the challenges associated with acquiring multiple image modalities for …
Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Alzheimer's diseases detection by using deep learning algorithms: a mini-review
S Al-Shoukry, TH Rassem, NM Makbol - IEEE Access, 2020 - ieeexplore.ieee.org
The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient
treatment, especially at the disease's early stages, because risk awareness allows the …
treatment, especially at the disease's early stages, because risk awareness allows the …
[HTML][HTML] A reusable benchmark of brain-age prediction from M/EEG resting-state signals
Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …
machine learning to large volumes of brain images. These measures of brain age, obtained …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses
L Snoek, MM van der Miesen, T Beemsterboer… - Scientific data, 2021 - nature.com
Abstract We present the Amsterdam Open MRI Collection (AOMIC): three datasets with
multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and …
multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and …
[HTML][HTML] Within and between-person correlates of the temporal dynamics of resting EEG microstates
Microstates reflect transient brain states resulting from the synchronous activity of brain
networks that predominate in the broadband EEG. There has been increasing interest in …
networks that predominate in the broadband EEG. There has been increasing interest in …
Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network
N Wagh, Y Varatharajah - Machine Learning for Health, 2020 - proceedings.mlr.press
This paper presents a novel graph convolutional neural network (GCNN)-based approach
for improving the diagnosis of neurological diseases using scalp-electroencephalograms …
for improving the diagnosis of neurological diseases using scalp-electroencephalograms …
Time-resolved parameterization of aperiodic and periodic brain activity
Macroscopic neural dynamics comprise both aperiodic and periodic signal components.
Recent advances in parameterizing neural power spectra offer practical tools for evaluating …
Recent advances in parameterizing neural power spectra offer practical tools for evaluating …
Diet modulates brain network stability, a biomarker for brain aging, in young adults
LR Mujica-Parodi, A Amgalan… - Proceedings of the …, 2020 - National Acad Sciences
Epidemiological studies suggest that insulin resistance accelerates progression of age-
based cognitive impairment, which neuroimaging has linked to brain glucose …
based cognitive impairment, which neuroimaging has linked to brain glucose …