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.

本研究针对深度卷积神经网络应用于结构性脑部 MRI 扫描检测阿尔茨海默病的问题，提出几种可以提升模型性能的技术，其中包括实例规范化、年龄信息等，这些技术的应用可将模型的准确率提高 14%。