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.
@article{arxiv.2406.04280,
title = {xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology},
author = {Julius Hense and Mina Jamshidi Idaji and Oliver Eberle and Thomas Schnake and Jonas Dippel and Laure Ciernik and Oliver Buchstab and Andreas Mock and Frederick Klauschen and Klaus-Robert Müller},
journal= {arXiv preprint arXiv:2406.04280},
year = {2025}
}