In this paper, we proposed a dimensional-attention-based 3D convolutional neural network (DACNN) to estimate the biological age for developing normal brain�...
In this paper, we proposed a dimensional-attention-based 3D convolutional neural network (DACNN) to estimate the biological age for developing normal brain�...
In this paper, we try to introduce a dimensional attention module into the classical 3D CNN framework, and estab- lish the dimensional-attention-based 3D�...
Deep learning (DL) has emerged as a popular strategy for brain age prediction, given its remarkable success in trans-domain image analysis problems and its�...
Dec 16, 2023 � We proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information.
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Mar 6, 2024 � Accurate brain age prediction model for healthy children and adolescents using 3D-CNN and dimensional attention. In: 2021 IEEE International�...
Aug 25, 2024 � This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA)�...
We found that AgeDiffuse brain age predictions reflected age-related brain structure volume changes better than biological age (R2 = 0.48 vs. R2 = 0.37).
Our results indicate that the refined brain age models showed better prediction accuracy compared to both pre-trained models and self-trained models.
Missing: Dimensional | Show results with:Dimensional
Jul 9, 2024 � In this work, we propose a robust brain age estimation paradigm that utilizes a 3D CNN model, by-passing the need for model-retraining across datasets.
