  Survival time prediction from medical images is important for treatment
planning, where accurate estimations can improve healthcare quality. One issue
affecting the training of survival models is censored data. Most of the current
survival prediction approaches are based on Cox models that can deal with
censored data, but their application scope is limited because they output a
hazard function instead of a survival time. On the other hand, methods that
predict survival time usually ignore censored data, resulting in an
under-utilization of the training set. In this work, we propose a new training
method that predicts survival time using all censored and uncensored data. We
propose to treat censored data as samples with a lower-bound time to death and
estimate pseudo labels to semi-supervise a censor-aware survival time
regressor. We evaluate our method on pathology and x-ray images from the
TCGA-GM and NLST datasets. Our results establish the state-of-the-art survival
prediction accuracy on both datasets.
