paper-with-me

홈 › Papers

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

2025-12-19 · Siemen Brussee, Pieter A. Valkema, Jurre A. J. Weijer, Thom Doeleman, Anne M. R. Schrader, Jesper Kers 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

📄 PDF Abstract BibTeX arXiv:2512.17517

Code (0)

등록된 구현이 없습니다.

Tasks

Multiple Instance Learning

Similar Papers 제목 키워드 기반

PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

2021-05-04 · Alexandru-Iosif Toma, Hao-Ya Hsueh, Hussein Ali Jaafar, Riku Murai 외

Path planning is a key component in mobile robotics. A wide range of path planning algorithms exist, but few attempts have been made to benchmark the algorithms holistically or unify their interface. Moreover, with the r…

Benchmarking

Systematic Comparison of Path Planning Algorithms using PathBench

2022-03-07 · Hao-Ya Hsueh, Alexandru-Iosif Toma, Hussein Ali Jaafar, Edward Stow 외

Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly-exploring random tree (RRT) are used heavily in autonomous robots. With the recent advances in…

Benchmarking

PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology

2025-05-26 · Jiabo Ma, Yingxue Xu, Fengtao Zhou, Yihui Wang 외

The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer diagnosis, and prognosis assessment. Whil…

BenchmarkingPrognosis

VEGA: Towards an End-to-End Configurable AutoML Pipeline

2020-11-03 · Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen 외

Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated AutoML system faces four great challenges of…

AutoMLBIG-bench Machine LearningData AugmentationDiversity+3

GAMA: a General Automated Machine learning Assistant

2020-07-09 · Pieter Gijsbers, Joaquin Vanschoren

The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoM…

AutoMLBenchmarkingBIG-bench Machine Learning