Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide
  Image Classification
Tingting Zheng Kui Jiang Hongxun Yao
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.