Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images

Lichao Xiao, Jin-Gang Yu, Zhifeng Liu, Jiarong Ou, Shule Deng, Zhenhua Yang, Yuanqing Li

Abstract: Survival prediction is a typical task in computer-aided diagnosis with many clinical applications. Existing approaches to survival prediction are mostly based on the classic Cox model, which mainly focus on learning a hazard or survival function rather than the survival time, largely limiting their practical uses. In this paper, we present a Censoring-Aware Deep Ordinal Regression (CDOR) to directly predict survival time from pathological images. Instead of relying on the Cox model, CDOR formulates survival prediction as an ordinal regression problem, and particularly introduces a censoring-aware loss function to train the deep network in the presence of censored data. Experiment results on publicly available dataset demonstrate that, the proposed CDOR can achieve significant higher accuracy in predicting survival time.