Biological Brain Age Estimation Using Multi-Task Self-Supervision and Pretrained Deep Neural Networks

Sajjadi, Zahrasadat; Hamzebeigi, Soheil; Soryani, Mohsen

doi:10.22044/jadm.2026.17592.2904

Document Type : Original/Review Paper

Authors

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

10.22044/jadm.2026.17592.2904

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

Main Subjects

H.5. Image Processing and Computer Vision

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Journal of AI and Data Mining

Biological Brain Age Estimation Using Multi-Task Self-Supervision and Pretrained Deep Neural Networks

References

References

Volume 14, Issue 4
October 2026
Pages 621-632

Biological Brain Age Estimation Using Multi-Task Self-Supervision and Pretrained Deep Neural Networks

References

References

Volume 14, Issue 4October 2026Pages 621-632

Volume 14, Issue 4
October 2026
Pages 621-632