[HTML][HTML] Deep learning based synthesis of MRI, CT and PET: Review and analysis

S Dayarathna, KT Islam, S Uribe, G Yang, M Hayat… - Medical image …, 2024 - Elsevier
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 …

Graph-based deep learning for medical diagnosis and analysis: past, present and future

D Ahmedt-Aristizabal, MA Armin, S Denman, C Fookes… - Sensors, 2021 - mdpi.com
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 …

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 …

[HTML][HTML] A reusable benchmark of brain-age prediction from M/EEG resting-state signals

DA Engemann, A Mellot, R Höchenberger, H Banville… - Neuroimage, 2022 - Elsevier
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 …

Self-supervised contrastive learning for medical time series: A systematic review

Z Liu, A Alavi, M Li, X Zhang - Sensors, 2023 - mdpi.com
Medical time series are sequential data collected over time that measures health-related
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 …

[HTML][HTML] Within and between-person correlates of the temporal dynamics of resting EEG microstates

AP Zanesco, BG King, AC Skwara, CD Saron - NeuroImage, 2020 - Elsevier
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 …

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 …

Time-resolved parameterization of aperiodic and periodic brain activity

LE Wilson, J da Silva Castanheira, S Baillet - Elife, 2022 - elifesciences.org
Macroscopic neural dynamics comprise both aperiodic and periodic signal components.
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 …