Robust brain age estimation based on smri via nonlinear age-adaptive ensemble learning
Precise prediction on brain age is urgently needed by many biomedical areas including
mental rehabilitation prognosis as well as various medicine or treatment trials. People …
mental rehabilitation prognosis as well as various medicine or treatment trials. People …
Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms
Y Joo, E Namgung, H Jeong, I Kang, J Kim, S Oh… - Scientific Reports, 2023 - nature.com
The clinical applications of brain age prediction have expanded, particularly in anticipating
the onset and prognosis of various neurodegenerative diseases. In the current study, we …
the onset and prognosis of various neurodegenerative diseases. In the current study, we …
A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer's diagnosis
KM Poloni, RJ Ferrari… - Expert Systems with …, 2022 - Elsevier
Age-associated diseases rise as life expectancy increases. The brain presents age-related
structural changes across life, with different extends between subjects and groups. During …
structural changes across life, with different extends between subjects and groups. During …
A skewed loss function for correcting predictive bias in brain age prediction
In neuroimaging, the difference between predicted brain age and chronological age, known
as brain age delta, has shown its potential as a biomarker related to various pathological …
as brain age delta, has shown its potential as a biomarker related to various pathological …
Robust Brain Age Estimation via Regression Models and MRI-derived Features
The determination of biological brain age is a crucial biomarker in the assessment of
neurological disorders and understanding of the morphological changes that occur during …
neurological disorders and understanding of the morphological changes that occur during …
Multimodal brain age prediction with feature selection and comparison
Brain age, an estimated biological age from anatomical and/or functional brain imaging
data, and its deviation from the chronological age (brain age gap) have shown the potential …
data, and its deviation from the chronological age (brain age gap) have shown the potential …
Predicting brain age using machine learning algorithms: A comprehensive evaluation
Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks.
The impact of regression algorithms on prediction accuracy in the brain age estimation …
The impact of regression algorithms on prediction accuracy in the brain age estimation …
Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets
Brain age estimation involves predicting an individual's biological age from their brain
images. This process offers valuable insights into the aging process and the progression of …
images. This process offers valuable insights into the aging process and the progression of …
Improving individual brain age prediction using an ensemble deep learning framework
Brain age is an imaging-based biomarker with excellent feasibility for characterizing
individual brain health and may serve as a single quantitative index for clinical and domain …
individual brain health and may serve as a single quantitative index for clinical and domain …
Kernel Ridge Regression-based Randomized Network for Brain Age Classification and Estimation
Accelerated brain aging and abnormalities are associated with variations in brain patterns.
Effective and reliable assessment methods are required to accurately classify and estimate …
Effective and reliable assessment methods are required to accurately classify and estimate …