mlsanityData to 14 Oct 2026

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

arXiv 2512.17517 ↗First posted 19 Dec 2025cs.CV cs.LG cs.NEcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 19 Dec 2025.

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References · 14

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  3. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  4. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
  5. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  6. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 470
  7. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 173
  8. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 150
  9. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 66
  10. 2025Accelerating Data Processing and Benchmarking of AI Models for Pathologycited by 65
  11. 2022Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potentialcited by 54
  12. 2024Graph Neural Networks in Histopathology: Emerging Trends and Future Directionscited by 13
2 more
  1. 2024Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspectivecited by 10
  2. 2023Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualizationcited by 7

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