AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification
Authors: Jonas Waibel, Frank Kramer, Dominik Müller
Organizations: IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany · Department of Computer Science, Ulm University of Applied Sciences, Ulm, Germany
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.