• Explainable Brain Age Prediction using coVariance Neural Networks🔍
• Explainable brain age prediction using covariance neural networks🔍
• Brain age prediction using the graph neural network based on ...🔍
• Brain age prediction using combined deep convolutional neural ...🔍
• Brain age prediction using interpretable multi|feature|based ...🔍
• Explainable Deep Learning for Personalized Age Prediction With ...🔍
• Brain age predicted using graph convolutional neural network ...🔍
• Comparative Evaluation of Interpretation Methods in Surface|Based ...🔍

Explainable Brain Age Prediction using coVariance Neural Networks

Title:Explainable Brain Age Prediction using coVariance Neural Networks ... Abstract:In computational neuroscience, there has been an increased ...

Explainable Brain Age Prediction using coVariance Neural Networks

By associating Δ -Age with a regional profile, VNNs also provide a feasible tool to distinguish pathologies if the distributions of Δ -Age for them are ...

Explainable brain age prediction using covariance neural networks

Abstract. In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain ...

Explainable Brain Age Prediction using coVariance Neural Networks

Predicting Brain Age using Transferable coVariance Neural Networks ... The deviation between chronological age and biological age is a well- ...

Explainable Brain Age Prediction using coVariance Neural Networks

In this paper, we leverage coVariance neural networks (VNN) to propose an anatomically interpretable framework for brain age prediction using ...

Brain age prediction using the graph neural network based on ...

Results: The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI ...

Brain age prediction using combined deep convolutional neural ...

Abbreviations: CamCAN, Cambridge Centre for Ageing and Neuroscience; CNN, convolutional neural network; MAE, mean absolute error; MLP, multi- ...

Brain age prediction using interpretable multi-feature-based ...

Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in ...

Explainable Deep Learning for Personalized Age Prediction With ...

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as ...

Brain age predicted using graph convolutional neural network ...

Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed ...

Comparative Evaluation of Interpretation Methods in Surface-Based ...

Based on prior studies, the Graph Convolutional Network (GCN) model has been shown to be particularly effective for exploring brain age ...

Brain age prediction using deep learning uncovers associated ...

Convolutional neural networks (CNNs) are deep learning techniques that are especially powerful for image processing and computer vision.

Accurate brain age prediction with lightweight deep neural networks

It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and ...

Brain age correlates with plasma NfL in amyloid positive individuals

Background: Brain age prediction using machine learning (ML) models provides novel opportunities to characterize neurobiology of accelerated aging.

Towards a Foundation Model for Brain Age Prediction ... - NASA ADS

Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks ... Abstract. Brain age is the estimate of biological age derived from ...

Biological brain age prediction using machine learning on structural ...

We computed the SHapley Additive exPlanation (SHAP) values, which reflect the marginal contribution of each brain region to the brain-age ...

Semi-Supervised Diffusion Model for Brain Age Prediction

Chronological age of healthy brain is able to be predicted using deep neural networks from T1-weighted magnetic resonance images (T1 MRIs), and the predicted ...

Brain Age Prediction: A Comparison between Machine Learning ...

Neuroimaging-based brain age is widely used to quantify an individual's brain health as deviation from a normative brain aging trajectory. Machine learning ...

Machine learning for brain age prediction: Introduction to methods ...

In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction.

Transferability of coVariance Neural Networks

Our recent work has demonstrated that VNNs can provide an anatomically interpretable perspective to the task of “brain age” prediction from ...