In this paper, we demonstrate the utility of VNNs in inferring brain age using cortical thickness data. Furthermore, our results show that VNNs exhibit multi- ...
ABSTRACT. The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration.
Step 2. Linear regression-based age-bias correct for outputs of ML model. Step 3. Obtain brain age gap for healthy controls and individuals with ...
In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction.
In this paper, we leverage coVariance neural networks (VNN) to propose an anatomically interpretable framework for brain age prediction using cortical thickness ...
May 2, 2023 · We show that VNNs exhibit transferability of performance over datasets whose covariance matrices converge to a limit object.
In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction ...
Oct 27, 2023 · In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction.
Missing: Transferable | Show results with:Transferable
May 27, 2023 · In this paper, we propose a principled framework for brain age prediction using cortical thickness data that accommodates interpretability by ...
Jul 3, 2024 · Our recent work has demonstrated that VNNs can provide an anatomically interpretable perspective to the task of “brain age” prediction from ...