AdvMIL：用于全切片图像生存分析的对抗性多示例学习
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images
AI总结：
提出即插即用的AdvMIL框架，将对抗性事件时间建模与MIL结合，用于WSI生存分析，实现分布估计、半监督利用未标注数据并提升噪声鲁棒性。
摘要：
组织病理学全切片图像（WSIs）上的生存分析是评估患者预后的最重要手段之一。尽管已经为千兆像素WSI开发了许多弱监督深度学习模型，但其潜力通常受到经典生存分析规则和全监督学习要求的限制。因此，这些模型只能为患者提供关于事件发生时间的完全确定的点估计，并且目前只能从小规模的已标注WSI数据中学习。为解决这些问题，我们提出了一种新颖的对抗性多示例学习（AdvMIL）框架。该框架基于对抗性事件发生时间建模，并整合了WSI表征学习中非常必要的多示例学习（MIL）。它是即插即用的，因此大多数现有的基于MIL的端到端方法都可以通过应用该框架轻松升级，从而获得更强的生存分布估计和半监督学习能力。大量实验表明，AdvMIL不仅通常能够以相对较低的计算成本为主流WSI生存分析方法带来性能提升，还能使这些方法通过半监督学习有效利用未标注数据。此外，观察到AdvMIL有助于提高模型对图像块遮挡和两种典型图像噪声的鲁棒性。所提出的AdvMIL框架以其新颖的对抗性MIL范式推动计算病理学中生存分析的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
