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.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant 61703166 and Grant 61633010, the National Key Research and Development Program of China under Grant 2017YFB1002505, the Guangdong Natural Science Foundation under Grant 2014A030312005, the National Key Basic Research Program of China (973 Program) under Grant 2015CB351703, the Guangzhou Science and Technology Program under Grant 201904010299, and the Fundamental Research Funds for the Central Universities, SCUT, under Grant 2018MS72.
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Xiao, L. et al. (2020). Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_43
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