Document Type : Original/Review Paper

Authors

• Zahrasadat Sajjadi ¹
• Soheil Hamzebeigi ¹
• Mohsen Soryani ²

¹ School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

² Iran University of Science and Technology

Abstract

As the global population ages, reliable methods for assessing brain health and age-related changes are increasingly important. Brain age is a promising biomarker of brain health, and machine-learning methods have enabled its estimation from neuroimaging data. However, effective training strategies are required for accurate brain age estimation. This study proposes a two-dimensional convolutional neural network (2D CNN)-based multi-task framework for estimating brain age from magnetic resonance imaging (MRI) scans in the ADNI dataset. The framework uses VGG-16 and U-Net encoder backbones with separate heads for age regression and image-rotation classification. We evaluated the effects of several pretraining strategies, including self-supervised pretraining using the DINO framework and supervised pretraining through brain-tumor segmentation. The best-performing configuration, consisting of a DINO-pretrained VGG-16 backbone and a multi-task prediction head, achieved a mean absolute error (MAE) of 3.27 years, which is competitive with previously reported methods. The results indicate that combining transfer learning with multi-task learning improved performance relative to the corresponding single-task models, suggesting that this combination supports the learning of richer and more generalizable feature representations.

Keywords

• Deep learning
• DINO
• Multi-task
• Pretraining
• Transfer learning

Main Subjects

• H.5. Image Processing and Computer Vision