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Building accurate Deep Learning (DL) models for brain age prediction is avery relevant topic in neuroimaging, as it could help better understandneurodegenerative disorders and find new biomarkers. To estimate accurate andgeneralizable models, large datasets have been collected, which are oftenmulti-site and multi-scanner. This large heterogeneity negatively affects thegeneralization performance of DL models since they are prone to overfitsite-related noise. Recently, contrastive learning approaches have been shownto be more robust against noise in data or labels. For this reason, we proposea novel contrastive learning regression loss for robust brain age predictionusing MRI scans. Our method achieves state-of-the-art performance on theOpenBHB challenge, yielding the best generalization capability and robustnessto site-related noise.

Contrastive learning for regression in multi-site brain age prediction