arXiv cs.AI· Sonja Topf, Ana Sanchez-Fernandez, G\"unter Klambauer·· 13 小时前AI 评分20
BatchBench：评估 AI 在生物医学图像批次效应与域偏移下鲁棒性的基准榜单
BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard
AI 导读
BatchBench 是一个基于 JUMP-CP 数据集构建的排行榜，用于评估和比较 AI 系统在未见实验批次与数据来源的生物医学图像上的鲁棒性。JUMP-CP 由 Cell Painting 检测生成，具有明显的批次效应和域偏移，构成检验分布变化鲁棒性的高难度测试平台。
正文 · 原文
Abstract:Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substantially different experimental conditions. Although recent domain adaptation methods have made substantial progress, significant performance gaps under domain shift persist. To measure progress in this regard, we introduce BatchBench, a leaderboard that evaluates and compares the robustness of AI systems on biomedical images from unseen experimental batches and data sources. BatchBench is built on JUMP-CP, one of the largest publicly available biomedical imaging datasets. Generated using the Cell Painting assay, JUMP-CP exhibits pronounced batch effects and domain shifts, making it a challenging testbed for evaluating robustness to distributional changes.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.30104 [cs.LG] |
| (or arXiv:2610.30104v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30104 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ana Sanchez-Fernandez [view email]
[v1]
Wed, 21 Oct 2026 17:50:22 UTC (183 KB)
来源：arXiv cs.AI · arxiv.org