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Found 7 papers, 2 papers with code
A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions.
Multimodal brain age estimation using interpretable adaptive population-graph learning
We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.
A Study of Demographic Bias in CNN-based Brain MR Segmentation
We find significant sex and race bias effects in segmentation model performance.
Voxel-level Importance Maps for Interpretable Brain Age Estimation
In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition.
Patch-based Brain Age Estimation from MR Images
Many studies have been proposed for the prediction of chronological age from brain MRI using machine learning and specifically deep learning techniques.
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation
We also show how a shift in domain of the training data from young and healthy towards older and more pathological examples leads to better segmentations of the latter cases, and that this leads to a significant improvement in the ability for the computed segmentations to stratify cases of AD.
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets.
