  Deep learning builds the foundation for many medical image analysis tasks
where neuralnetworks are often designed to have a large receptive field to
incorporate long spatialdependencies. Recent work has shown that large
receptive fields are not always necessaryfor computer vision tasks on natural
images. We explore whether this translates to certainmedical imaging tasks such
as age and sex prediction from a T1-weighted brain MRI scans.
