Deep correlational learning for survival prediction from multi-modality data

Abstract

Technological advances have created a great opportunity to provide multi-view data for patients. However, due to the large discrepancy between different heterogeneous views, traditional survival models are unable to efficiently handle multiple modalities data as well as learn very complex interactions that can affect survival outcomes in various ways. In this paper, we develop a Deep Correlational Survival Model (DeepCorrSurv) for the integration of multi-view data. The proposed network consists of two sub-networks, view-specific and common sub-network. To remove the view discrepancy, the proposed DeepCorrSurv first explicitly maximizes the correlation among the views. Then it transfers feature hierarchies from view commonality and specifically fine-tunes on the survival regression task. Extensive experiments on real lung and brain tumor data sets demonstrated the effectiveness of the proposed DeepCorrSurv model using multiple modalities data across different tumor types.