  Early detection is a crucial goal in the study of Alzheimer's Disease (AD).
In this work, we describe several techniques to boost the performance of 3D
deep convolutional neural networks (CNNs) trained to detect AD using structural
brain MRI scans. Specifically, we provide evidence that (1) instance
normalization outperforms batch normalization, (2) early spatial downsampling
negatively affects performance, (3) widening the model brings consistent gains
while increasing the depth does not, and (4) incorporating age information
yields moderate improvement. Together, these insights yield an increment of
approximately 14% in test accuracy over existing models when distinguishing
between patients with AD, mild cognitive impairment, and controls in the ADNI
dataset. Similar performance is achieved on an independent dataset.
