English

PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning in Histopathology

Computer Vision and Pattern Recognition 2025-12-22 v1 Machine Learning Neural and Evolutionary Computing Software Engineering Tissues and Organs

Abstract

We introduce PathBench-MIL, an open-source AutoML and benchmarking framework for multiple instance learning (MIL) in histopathology. The system automates end-to-end MIL pipeline construction, including preprocessing, feature extraction, and MIL-aggregation, and provides reproducible benchmarking of dozens of MIL models and feature extractors. PathBench-MIL integrates visualization tooling, a unified configuration system, and modular extensibility, enabling rapid experimentation and standardization across datasets and tasks. PathBench-MIL is publicly available at https://github.com/Sbrussee/PathBench-MIL

Keywords

Cite

@article{arxiv.2512.17517,
  title  = {PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning in Histopathology},
  author = {Siemen Brussee and Pieter A. Valkema and Jurre A. J. Weijer and Thom Doeleman and Anne M. R. Schrader and Jesper Kers},
  journal= {arXiv preprint arXiv:2512.17517},
  year   = {2025}
}

Comments

14 Pages, 3 Figures, 2 Appendices

R2 v1 2026-07-01T08:33:21.322Z