Predicting Brain Age Using Tri-unet And Various Mri Scale Features

This article discusses the importance of accurately predicting brain age in order to assess brain aging, screen for disease risks, and diagnose age-related diseases. The proposed method, Tri-UNet, utilizes a multi-scale feature fusion approach and a brain region information fusion method to effectively learn features from different scales and regions of the brain. Experiments on the Cam-CAN dataset show promising results with a minimum Mean Absolute Error of 7.46. This method has potential for practical applications in elderly education.