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arXiv · 2610.32452

AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification

Jonas Waibel·Frank Kramer·Dominik Müller
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Abstract

Developing multiple instance learning pipelines for the classification of whole-slide images involves many interdependent design decisions and expertise in both pathology and machine learning. We present AutoMIL, an open-source framework that applies principles from the field of automated machine learning to tackle this task by automating integral pipeline steps, such as preprocessing, training and evaluation. In a benchmark evaluation with default settings, AutoMIL reached ROC-AUC scores of 0.94 on UBC-OCEAN, 0.99 on PCam and 0.93-0.96 on a TCGA-NSCLC subset, comparable to specialised pipelines in related work. Ensembling mainly improved performance on imbalanced data, showing that an automated pipeline must adapt to dataset characteristics. AutoMIL thus provides an accessible baseline for whole-slide image classification without manual pipeline configuration.

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Jonas Waibel, Frank Kramer, Dominik Müller. 2026-10-23. AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification. https://arxiv.org/abs/2610.32452

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