xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

Published: 25 Sept 2024, Last Modified: 07 Jan 2025NeurIPS 2024 posterEveryoneRevisionsBibTeXCC BY-NC-ND 4.0
Keywords: computational pathology, multiple instance learning, explainable AI, layer-wise relevance propagation, knowledge discovery
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.
Primary Area: Machine learning for healthcare
Submission Number: 16014
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