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.

翻译：显著性图谱已被广泛用于解释阿尔茨海默病（AD）的深度学习分类器。然而，由于AD具有异质性且存在多种亚型，其病理机制尚未被完全阐明，且可能因患者而异。由于缺乏对此机制的充分理解，难以全面有效地评估AD分类器的显著性图谱。本文利用解剖分割将显著性值分配至不同脑区。通过绘制对应于AD与正常对照（NC）的显著性图谱分布，我们可以全面了解模型的决策过程。为了利用AD患者在疾病进展过程中出现脑体积萎缩这一事实，我们定义了一种新的评估指标——脑体积变化评分（VCS），该指标通过计算每位患者在不同脑区中脑体积变化与模型显著性值之间的平均皮尔逊相关系数得到。因此，VCS指标有助于我们理解不同模型产生的显著性图谱如何与全脑不同区域的体积变化相关联。我们在ADNI数据集上训练候选模型，并在三个不同数据集上进行测试。结果表明：（i）具有较高VCS的模型倾向于展示出更多与AD病理相关的细节的显著性图谱；（ii）采用基于梯度的对抗训练策略（如FGSM和随机掩码）能够提升模型的VCS。