  Saliency maps have been widely used to interpret deep learning classifiers
for Alzheimer's disease (AD). However, since AD is heterogeneous and has
multiple subtypes, the pathological mechanism of AD remains not fully
understood and may vary from patient to patient. Due to the lack of such
understanding, it is difficult to comprehensively and effectively assess the
saliency map of AD classifier. In this paper, we utilize the anatomical
segmentation to allocate saliency values into different brain regions. By
plotting the distributions of saliency maps corresponding to AD and NC (Normal
Control), we can gain a comprehensive view of the model's decisions process. In
order to leverage the fact that the brain volume shrinkage happens in AD
patients during disease progression, we define a new evaluation metric, brain
volume change score (VCS), by computing the average Pearson correlation of the
brain volume changes and the saliency values of a model in different brain
regions for each patient. Thus, the VCS metric can help us gain some knowledge
of how saliency maps resulting from different models relate to the changes of
the volumes across different regions in the whole brain. We trained candidate
models on the ADNI dataset and tested on three different datasets. Our results
indicate: (i) models with higher VCSs tend to demonstrate saliency maps with
more details relevant to the AD pathology, (ii) using gradient-based
adversarial training strategies such as FGSM and stochastic masking can improve
the VCSs of the models.
