SQET: Squeeze and Excitation Transformer for High-accuracy Brain Age Estimation

The aging process of human brain is complex, which can result in brain structural changes. One promising way to gain a deep understanding of aging process is using machine learning, typically convolutional neural network (CNN), to predict brain age based on magnetic resonance imaging data. Though CNN has a strong ability to capture features from a small local region of the input image, it lacks the ability to capture global features of the surrounding neighbors. Thus, in this paper, we propose the squeeze and excitation transformer (SQET) for pursuing high-accuracy brain age estimation, in which a squeeze and excitation module is designed and fused in conventional self-attention in the transformer structure to capture global features among different localities even if they are spatially far apart. In particular, for 9 public datasets with 6,318 healthy brain Tl-MRIs with an age range of 6-88, our proposed SQET can achieve the result of 2.55 MAE and the correlation coefficient r=0.983, which has significantly outperformed all other reported models up to now.