The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.
翻译:组织整体切片图像(WSIs)上的生存分析是估计患者预后的最重要手段之一。尽管已经开发了许多基于深度学习的弱监督模型用于处理大规模WSIs,但其潜力通常受到传统生存分析规则和完全监督学习要求的限制。因此,这些模型仅为患者提供完全确定的时间推测,并且目前只能从标记的WSI数据中进行学习。为了解决这些问题,我们提出了一种新的对抗性多实例学习(AdvMIL)框架。该框架基于对抗性时间推测模型,融合了多实例学习(MIL),这对于WSI表征学习非常必要。这是一个即插即用的方法,因此,大多数现有的基于MIL的端到端方法都可以轻松地通过应用此框架进行升级,从而获得生存分布估计和半监督学习的改进能力。我们的广泛实验表明,AdvMIL不仅经常能够在相对较低的计算成本下为主流WSI生存分析方法带来性能改进,而且能够使这些方法通过半监督学习有效地利用未标记数据。此外,我们观察到,AdvMIL可以帮助改善模型针对补丁遮挡和两种典型图像噪声的鲁棒性。所提出的AdvMIL框架可以通过其新颖的对抗性MIL范式促进计算病理学中的生存分析研究。