Survival analysis via transduction for semi-supervised neural networks in medical prognosis

The central challenge in predictive modeling for survival analysis in medical prognostics is managing censored observations. Traditional regression techniques are challenged by these censored samples. In such problems the true target times of a majority of instances are unknown; what is known is a censored target representing some earlier indeterminate time. Such censored samples can be considered as semi-supervised targets; however most efforts in semi-supervised regression do not take into account the partial nature of unsupervised information; samples are treated as either fully labelled or unlabeled. In this work we extend a novel transduction approach for semi-supervised survival analysis to neural networks. The true target times are approximated from the censored times through transduction. For prostate and breast cancer applications, this semi-supervised regression framework yields a significant improvement in performance.