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

Understand

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.