A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

Abstract

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.
Publication
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)
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Sebastian Pölsterl
Post-Doctoral Researcher

My research interests include machine learning for time-to-event analysis, non-Euclidean data, and biomedical applications.