Dataset-adaptive and bias-constrained brain age estimation using pyramid squeeze and excitation transformer

Modeling the biological changes of the human brain is crucial for identifying brain-related diseases and health monitoring. The brain age predicted from MRI data is one useful biomarker for quantifying the maturation and ageing process of human brain. However, the acquisition and preprocessing of MRI data can introduce significant variations between datasets, making it essential to develop models with higher accuracy and robustness for cross-dataset evaluation. To achieve this goal, our paper combines the strengths of CNNs and transformers, proposing the Pyramid Squeeze and Excitation Transformer (PSET) as a novel approach for brain age estimation. In the PSET framework, 3D inception blocks function as an advanced CNN module to capture localized features while the self-attention mechanism is integrated with a squeeze-and-excitation module to extract global features across disparate patches. In particular, a dataset-adaptive and bias-constrained (DABC) model training strategy is proposed to improve the robustness for cross-dataset situations and reduce the bias by introducing self-supervised pre-training, meta-learning and novel loss functions. Experiment results on the dataset of 15,437 healthy brain T1-MRIs (MAE=2.342), demonstrated that the proposed method outperforms both classic visual models and existing brain age estimation models, in the aspect of accuracy, generality and unbiasedness. Additionally, through visualization analysis, we identified the key brain regions that play significant roles in brain age estimation, including the occipital lobe. We compared the brain age gap between patients with diseases and healthy control groups, demonstrating the phenomenon of abnormal aging in conditions such as Alzheimer’s disease and mild cognitive impairment.