  Building accurate Deep Learning (DL) models for brain age prediction is a
very relevant topic in neuroimaging, as it could help better understand
neurodegenerative disorders and find new biomarkers. To estimate accurate and
generalizable models, large datasets have been collected, which are often
multi-site and multi-scanner. This large heterogeneity negatively affects the
generalization performance of DL models since they are prone to overfit
site-related noise. Recently, contrastive learning approaches have been shown
to be more robust against noise in data or labels. For this reason, we propose
a novel contrastive learning regression loss for robust brain age prediction
using MRI scans. Our method achieves state-of-the-art performance on the
OpenBHB challenge, yielding the best generalization capability and robustness
to site-related noise.
