  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.
