Figure 2.

ResNet-50 architecture for brain age regression with attention-guided mask inference. A single sagittal image with dimensions 224 × 224 is shown as an input to the global branch. Architectures incorporating multiple slices and planes are not displayed. The input of the local branch is a weighted image isolating the region of interest automatically generated from attention-guided mask inference. Global and local branches contain five convolutional layers (conv1 to conv5), each consisting of 3–6 building blocks (boxes) with a convolution, batch normalization, and rectified linear unit (ReLU), streamlined by shortcut connections (gray dotted arrows). Output sizes are denoted by k × k. Feature maps from both branches enter a max pooling layer and are subsequently fed to a fully connected layer (fc). The MSE for each branch and the total loss are minimized via gradient descent (black dotted arrows), simultaneously tuning model weights for both local and global branches via backpropagation. Age predictions (GA) are generated from each branch and averaged to produce the final age estimation.