Brain Age Prediction via Self-Supervised Multitask Learning with Cross-Modal Fusion

Abstract

Accurate brain age estimation from structural MRI serves as a sensitive biomarker of neurological health. We propose NeuroFusion, a unified framework that addresses two fundamental limitations of existing approaches: vanishing gradients in deep 3D architectures and poor generalization across clinical settings with limited labelled data. NeuroFusion combines (1) dual-objective self-supervised pretraining (SimCLR + MAE) on 42,000 unlabeled volumes, (2) a 3D Vision Transformer backbone, (3) a cross-modal fusion module integrating demographic metadata, and (4) deeply-supervised multitask learning with uncertainty-weighted loss balancing. On the OpenBHB benchmark (N=3,966 healthy controls), NeuroFusion achieves state-of-the-art brain age prediction (MAE = 2.84 years). Crucially, in few-shot adaptation to unseen sites, NeuroFusion maintains strong performance with only 5 labelled examples, demonstrating clinically relevant generalizatio