Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images

TL;DR

This paper introduces a censor-aware semi-supervised learning method for survival time prediction from medical images, effectively utilizing censored data to improve accuracy beyond existing models.

Contribution

It proposes a novel training approach that leverages all censored and uncensored data by estimating pseudo labels, enhancing survival time prediction from medical images.

Findings

Achieves state-of-the-art accuracy on TCGA-GM and NLST datasets.

Effectively utilizes censored data in survival prediction.

Outperforms traditional Cox-based models in survival time estimation.

Abstract

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.