BatchBench 发布：基于 JUMP-CP 测评 AI 对生物医学图像批效应的鲁棒性
BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard
做生物影像的朋友可以关注，arXiv 上新出的 BatchBench 排行榜，用 JUMP-CP 数据集专门测模型换个实验批次还稳不稳。
研究人员推出 BatchBench，一个评测 AI 系统在未见过的实验批次和数据来源上鲁棒性的排行榜。BatchBench 基于 JUMP-CP 数据集构建，后者是采用 Cell Painting 实验方法的最大公开生物医学成像数据集之一。JUMP-CP 存在明显的批效应和域偏移，因此能作为评估分布变化下模型表现的高难度测试平台。论文指出，现有域适应方法在域偏移下仍存在显著性能差距。
BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard
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.