The human brain has presented age-related morphological changes, which enables the estimation of a subject’s age from a brain magnetic resonance (MR) image. Recently, various CNN-based age estimation methods from MR images have been proposed. Among them, Squeeze-and-Excitation Transformer (SQET)1) is known to extract long-range local features, while does not consider the global morphological differences, such as those related to gender. We propose an age estimation method that integrates gender information into the SQET framework. Specifically, we extend the final fully connected layer in SQET and estimate age by concatenating gender information to the image features. We also incorporate data augmentation to enhance model generalization and a ranking loss to enforce the correct order of estimated ages.