Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligencePredicting brain age from structural MRI (sMRI) is potentially valuable as the deviation of predicted age from chronological age can be a biomarker for characterising brain health conditions. Currently, extensive pre-processing of sMRI data is required for most deep learning methods. This study presents a multi-task contrastive learning framework for simultaneous brain age prediction and gender classification from minimally processed, noisy 3D T1-weighted images. By including gender classification task and supervised contrastive learning, we demonstrate that leveraging gender information in training and better representation learning can boost age prediction accuracy for both in-domain and out-of-domain datasets.
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