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.
翻译:从医疗图像中预测存活时间对于治疗规划十分重要,因为准确的估计可以提高医疗质量。影响生存模型培训的一个问题就是审查数据。目前大多数生存预测方法都以Cox模型为基础,这些模型可以处理受审查的数据,但其应用范围有限,因为它们产生危险功能而不是生存时间。另一方面,预测存活时间的方法通常忽视受审查的数据,导致对培训成套材料的利用不足。在这项工作中,我们提出了一个新的培训方法,利用所有受审查的和未经审查的数据预测存活时间。我们提议将受审查的数据作为受审查数据样本处理,将受审查数据作为下限死亡时间的样本,并估计假标签,以半监督具有审查意识的存活时间回归器。我们评估我们从TCGA-GM和NLST数据集获取的病理学和X射线图像的方法。我们的结果确定了两个数据集的最新生存预测准确性。