Three-dimensional end-to-end deep learning for brain MRI analysis
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Deep learning (DL) methods are increasingly outperforming classical
approaches in brain imaging, yet their generalizability across diverse imaging
cohorts remains inadequately assessed. As age and sex are key neurobiological
markers in clinical neuroscience, influencing brain structure and disease risk,
this study evaluates three of the existing three-dimensional architectures,
namely Simple Fully Connected Network (SFCN), DenseNet, and Shifted Window
(Swin) Transformers, for age and sex prediction using T1-weighted MRI from four
independent cohorts: UK Biobank (UKB, n=47,390), Dallas Lifespan Brain Study
(DLBS, n=132), Parkinson's Progression Markers Initiative (PPMI, n=108 healthy
controls), and Information eXtraction from Images (IXI, n=319). We found that
SFCN consistently outperformed more complex architectures with AUC of 1.00
[1.00-1.00] in UKB (internal test set) and 0.85-0.91 in external test sets for
sex classification. For the age prediction task, SFCN demonstrated a mean
absolute error (MAE) of 2.66 (r=0.89) in UKB and 4.98-5.81 (r=0.55-0.70) across
external datasets. Pairwise DeLong and Wilcoxon signed-rank tests with
Bonferroni corrections confirmed SFCN's superiority over Swin Transformer
across most cohorts (p<0.017, for three comparisons). Explainability analysis
further demonstrates the regional consistency of model attention across cohorts
and specific to each task. Our findings reveal that simpler convolutional
networks outperform the denser and more complex attention-based DL
architectures in brain image analysis by demonstrating better generalizability
across different datasets.
