Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification

Tingting Zheng, Kui Jiang, Hongxun Yao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 8028-8037

Multi-Instance Learning (MIL) has shown impressive performance for histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves instance sampling feature representation and decision-making. However existing MIL-based technologies at least suffer from one or more of the following problems: 1) requiring high storage and intensive pre-processing for numerous instances (sampling); 2) potential over-fitting with limited knowledge to predict bag labels (feature representation); 3) pseudo-bag counts and prior biases affect model robustness and generalizability (decision-making). Inspired by clinical diagnostics using the past sampling instances can facilitate the final WSI analysis but it is barely explored in prior technologies. To break free these limitations we integrate the dynamic instance sampling and reinforcement learning into a unified framework to improve the instance selection and feature aggregation forming a novel Dynamic Policy Instance Selection (DPIS) scheme for better and more credible decision-making. Specifically the measurement of feature distance and reward function are employed to boost continuous instance sampling. To alleviate the over-fitting we explore the latent global relations among instances for more robust and discriminative feature representation while establishing reward and punishment mechanisms to correct biases in pseudo-bags using contrastive learning. These strategies form the final Dynamic Policy-Driven Adaptive Multi-Instance Learning (PAMIL) method for WSI tasks. Extensive experiments reveal that our PAMIL method outperforms the state-of-the-art by 3.8% on CAMELYON16 and 4.4% on TCGA lung cancer datasets.