自动化病理图像多实例学习流程，支持模型快速对比与复现
PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning in Histopathology
- 自动构建从预处理到特征聚合的完整MIL流水线
- 在多个数据集上对数十种MIL模型进行可复现基准测试
- 适合病理图像分析研究者快速实验与标准对比
我们提出PathBench-MIL，一个开源的自动机器学习与基准测试框架，专为组织病理学中的多实例学习（MIL）设计。该系统自动化完成端到端的MIL流程，包括预处理、特征提取和MIL聚合，并提供对数十种MIL模型与特征提取器的可复现基准测试。PathBench-MIL集成可视化工具、统一配置系统与模块化扩展能力，支持跨数据集和任务的快速实验与标准化。项目已公开，地址为 https://github.com/Sbrussee/PathBench-MIL。
原文摘要 · Abstract (English)
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
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