We introduce a wide and deep neural network for prediction of progression
from patients with mild cognitive impairment to Alzheimer's disease.
Information from anatomical shape and tabular clinical data (demographics,
biomarkers) are fused in a single neural network. The network is invariant to
shape transformations and avoids the need to identify point correspondences
between shapes. To account for right censored time-to-event data, i.e., when it
is only known that a patient did not develop Alzheimer's disease up to a
particular time point, we employ a loss commonly used in survival analysis. Our
network is trained end-to-end to combine information from a patient's
hippocampus shape and clinical biomarkers. Our experiments on data from the
Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model
is able to learn a shape descriptor that augments clinical biomarkers and
outperforms a deep neural network on shape alone and a linear model on common
clinical biomarkers.

研究利用神经网络将患者的 hippocampus 形态和临床生物标记结合，预测从轻度认知障碍到阿尔茨海默病的进展，并且在处理时间数据时采用了常用的生存分析方法。实验表明，该模型的表现优于处理形态数据以及处理临床生物标记数据的模型。