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Found 8 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.
Extended Graph Assessment Metrics for Graph Neural Networks
In this work, we introduce extended graph assessment metrics (GAMs) for regression tasks and continuous adjacency matrices.
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.
GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation
To address such limitations and alleviate the computational burden, we propose a learnable network to approximate geodesic paths.
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.
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging.
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.
