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：基于组织学全切片图像（WSIs）的生存分析是评估患者预后最重要的手段之一。尽管目前已有许多弱监督深度学习模型应用于十亿像素级WSIs，但其潜力通常受限于经典生存分析规则和完全监督学习要求。因此，现有模型仅能为患者提供完全确定的事件发生时间点估计，且只能从当前小规模标注WSI数据中进行学习。为解决这些问题，我们提出了一种新颖的自注意力多实例学习（AdvMIL）框架。该框架基于对抗性时间事件建模方法，并融合了对WSI表征学习至关重要的多实例学习（MIL）。其即插即用的特性使得大多数基于MIL的端到端方法可通过该框架轻松升级，从而获得生存分布估计和半监督学习能力的提升。广泛实验表明，AdvMIL不仅能够以较低计算成本为主流WSI生存分析方法带来性能提升，还能通过半监督学习使这些方法有效利用未标注数据。此外，观察发现AdvMIL有助于提升模型对图像块遮挡及两种典型图像噪声的鲁棒性。所提出的AdvMIL框架将以其创新的对抗性MIL范式推动计算病理学中生存分析的研究。