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.
Data and Machine Learning Advances with Multiple Views Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)