AutoMIL 开源框架自动完成全切片图像分类流水线
AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification
做病理图像分析的朋友可以看看，AutoMIL 把预处理到评估全自动化，不用手动配置也能跑到接近专业流水线的分数。
AutoMIL 是一个开源框架，将自动机器学习方法应用于全切片图像（WSI）分类的 MIL 流水线，自动处理预处理、训练和评估等环节。在默认设置下，AutoMIL 在 UBC-OCEAN 上取得 ROC-AUC 0.94，在 PCam 上达到 0.99，在 TCGA-NSCLC 子集上为 0.93-0.96，与人工调优的专业流水线相当。实验还发现集成（ensembling）主要在不平衡数据上带来提升，说明自动流水线需要根据数据集特点自适应调整。
AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification
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.