  Multiple instance learning (MIL) is an effective and widely used approach for
weakly supervised machine learning. In histopathology, MIL models have achieved
remarkable success in tasks like tumor detection, biomarker prediction, and
outcome prognostication. However, MIL explanation methods are still lagging
behind, as they are limited to small bag sizes or disregard instance
interactions. We revisit MIL through the lens of explainable AI (XAI) and
introduce xMIL, a refined framework with more general assumptions. We
demonstrate how to obtain improved MIL explanations using layer-wise relevance
propagation (LRP) and conduct extensive evaluation experiments on three toy
settings and four real-world histopathology datasets. Our approach consistently
outperforms previous explanation attempts with particularly improved
faithfulness scores on challenging biomarker prediction tasks. Finally, we
showcase how xMIL explanations enable pathologists to extract insights from MIL
models, representing a significant advance for knowledge discovery and model
debugging in digital histopathology.
