Contrastive learning for regression in multi-site brain age prediction

2022/11/14 by Carlo Alberto Barbano, Barbano, Carlo Alberto, Benoît Dufumier +7 · 2 citations
Medicine · Computer Science · #Fetal and Pediatric Neurological Disorders #Domain Adaptation and Few-Shot Learning #Neonatal and fetal brain pathology

paper · pdf · doi:10.48550/arxiv.2211.08326

Abstract

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.

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• MRI-Based Brain Age Estimation with Supervised Contrastive Learning of Continuous Representation
• Robust brain age estimation from structural MRI with contrastive learning

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