# Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T1-weighted magnetic resonance imaging

arXiv: https://arxiv.org/abs/2508.01565 · HTML: https://mlsanity.com/paper/2508.01565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Unsupervised domain adaptation for medical imaging segmentation with self-ensembling](https://mlsanity.com/paper/1811.06042/) (2018)
- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss](https://mlsanity.com/paper/2106.03052/) (2021)
- [Deep Relation Learning for Regression and Its Application to Brain Age Estimation](https://mlsanity.com/paper/2204.06598/) (2022)
- [Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs](https://mlsanity.com/paper/2401.09475/) (2024)
- [Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages](https://mlsanity.com/paper/2412.05632/) (2024)
- [Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis](https://mlsanity.com/paper/2209.00439/) (2022)
- [A voxel-level approach to brain age prediction: A method to assess regional brain aging](https://mlsanity.com/paper/2310.11385/) (2023)
- [Estimating Brain Age with Global and Local Dependencies](https://mlsanity.com/paper/2209.08933/) (2022)
- [Robust Brain Age Estimation via Regression Models and MRI-derived Features](https://mlsanity.com/paper/2306.05514/) (2023)

## Abstract (arXiv)

Accurate estimation of biological brain age from three dimensional (3D) T1-weighted magnetic resonance imaging (MRI) is a critical imaging biomarker for identifying accelerated aging associated with neurodegenerative diseases. Effective brain age prediction necessitates training 3D models to leverage comprehensive insights from volumetric MRI scans, thereby fully capturing spatial anatomical context. However, optimizing deep 3D models remains challenging due to problems such as vanishing gradients. Furthermore, brain structural patterns differ significantly between sexes, which impacts aging trajectories and vulnerability to neurodegenerative diseases, thereby making sex classification crucial for enhancing the accuracy and generalizability of predictive models. To address these challenges, we propose a Deeply Supervised Multitask Autoencoder (DSMT-AE) framework for brain age estimation. DSMT-AE employs deep supervision, which involves applying supervisory signals at intermediate layers during training, to stabilize model optimization, and multitask learning to enhance feature representation. Specifically, our framework simultaneously optimizes brain age prediction alongside auxiliary tasks of sex classification and image reconstruction, thus effectively capturing anatomical and demographic variability to improve prediction accuracy. We extensively evaluate DSMT-AE on the Open Brain Health Benchmark (OpenBHB) dataset, the largest multisite neuroimaging cohort combining ten publicly available datasets. The results demonstrate that DSMT-AE achieves state-of-the-art performance and robustness across age and sex subgroups. Additionally, our ablation study confirms that each proposed component substantially contributes to the improved predictive accuracy and robustness of the overall architecture.