AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images

TL;DR

AdvMIL introduces an adversarial multiple instance learning framework that enhances survival analysis on whole-slide images by enabling semi-supervised learning, improving robustness, and integrating with existing methods for better survival distribution estimation.

Contribution

This paper presents a novel adversarial MIL framework that improves survival analysis on WSIs, allowing semi-supervised learning and compatibility with existing models.

Findings

Performance improvements on mainstream methods

Effective utilization of unlabeled data

Enhanced robustness against noise and occlusion

Abstract

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.