xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

TL;DR

xMIL introduces a novel explainability framework for multiple instance learning in histopathology, significantly improving the faithfulness and utility of explanations for complex biomedical tasks.

Contribution

The paper presents xMIL, a generalized MIL explanation method using layer-wise relevance propagation, enhancing interpretability in histopathology applications.

Findings

xMIL outperforms previous explanation methods in faithfulness scores

It enables better insights for pathologists in histopathology models

The approach is validated on multiple real-world datasets

Abstract

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. Codes are available at: https://github.com/bifold-pathomics/xMIL.