arXiv cs.AI· Jonas Waibel, Frank Kramer, Dominik M\"uller·· 12 小时前AI 评分31
AutoMIL：面向全切片图像分类的自动化多实例学习框架
AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification
AI 导读
AutoMIL 是一个开源框架，将自动化机器学习原则用于全切片图像分类，自动完成预处理、训练与评估等流水线步骤。默认设置下基准测试达到 UBC-OCEAN ROC-AUC 0.94、PCam 0.99、TCGA-NSCLC 子集 0.93-0.96，与相关工作中的专用流水线相当。集成主要在不平衡数据上带来提升，表明自动化流水线需适配数据集特性。
正文 · 原文
Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32452 [cs.CV] |
| (or arXiv:2610.32452v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32452 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dominik Müller [view email]
[v1]
Fri, 23 Oct 2026 10:17:42 UTC (236 KB)
来源：arXiv cs.AI · arxiv.org