  In recent years, many papers have reported state-of-the-art performance on
Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset using convolutional neural networks.
However, we discover that when we split that data into training and testing
sets at the subject level, we are not able to obtain similar performance,
bringing the validity of many of the previous studies into question.
Furthermore, we point out that previous works use different subsets of the ADNI
data, making comparison across similar works tricky. In this study, we present
the results of three splitting methods, discuss the motivations behind their
validity, and report our results using all of the available subjects.
