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

Molla Md Rony, Rahman Md Takibur, Meheraj Hossain Dipu, Jubayer Ahmad Nurshi

Brain age estimation, Multitask learning, Deep supervision, Magnetic resonance imaging, Vision Transformer

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 generalization.

Received: 16 Jul 2026, Received in revised form: 14 Aug 2026, Accepted: 18 Aug 2026, Available online: 23 Aug 2026

10.22161/ijaers.138.6