Improved Transformer Attention Neural Network for Cognitive Estimation from Brain sMRI

Brain sMRI-based cognitive estimation of dementia states holds significant clinical value, aiding in assessing AD pathological staging and predicting disease progression. Some existing deep learning methods do not fully integrate the characteristics of brain image, which limits the effective representation of brain image and thus weakens its performance in regression and classification tasks. In this work, we propose a neural network that combining with Transformer attention to achieve cognitive estimation. In the proposed network, we design a Transformer block based on the long-range dependency and position encoding capabilities of Transformer to adapt to brain image and effectively combine it in the regression or classification task. We adapt Transformer attention for MRI feature by incorporating 3D positional encoding and 3D convolution. Our method can automatically identify sensitive regions from whole brain sMRI for end-to-end feature learning. Furthermore, it improves attention to AD sensitive biomarkers and improves the performance of regression tasks to jointly predict multiple cognitive scores. Evaluation on ADNI data sets shows promising performance of our method in cognitive scores estimation and AD classification.