CryoAnomaly：基于异常引导难负样本抑制的小样本冷冻电镜粒子挑选方法
CryoAnomaly: Few-Shot Cryo-EM Particle Picking via Anomaly-Guided Hard Negative Suppression
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中文总结 AI 辅助
本研究提出CryoAnomaly框架，利用合成数据解决小样本冷冻电镜粒子挑选中真实显微图像污染导致的误报问题，在CryoPPP基准小样本设置下实现最优挑选准确率与重建分辨率。
中文摘要 AI 辅助
冷冻电子显微镜（cryo-EM）对分析三维生物结构至关重要，其中自动粒子挑选是该流程的核心环节。然而全监督方法需要大量手动标注，尽管小样本学习提供了潜在解决方案，但现有方法因负样本监督不足，难以处理真实显微图像中固有的多样污染，导致误报降低了三维重建质量。尽管合成数据能提供丰富且完美的标签，但其应用主要局限于验证相同蛋白或扩充全样本训练，小样本场景下对新蛋白的适配潜力尚未被探索。本研究探讨合成数据在小样本粒子挑选中的有效利用，发现直接迁移因“干净-污染”的仿真到真实（Sim2Real）差距而失败。为克服该问题，我们提出CryoAnomaly框架，将此差距转化为优势：通过在干净合成数据上训练的异常检测器，将真实世界污染识别为异常，并通过新颖的异常引导难负样本抑制损失对其进行抑制。在CryoPPP基准测试中，CryoAnomaly在小样本设置下实现了优于现有方法的最佳挑选准确率和重建分辨率；所提异常引导损失在具有多样污染的数据集上被证实有效，可生成可靠的伪异常掩码。我们的代码、数据集和项目页面可在以下链接获取：this https URL、this https URL、this https URL。
英文摘要
Cryo-electron microscopy (cryo-EM) is crucial for analyzing 3D biological structures, in which automated particle picking is essential for the workflow. However, fully supervised methods require extensive manual annotations. While few-shot learning offers a potential solution, existing approaches struggle to handle the diverse contaminations inherent in real micrographs owing to insufficient negative supervision, resulting in false positives that degrade the quality of the 3D reconstruction. Although synthetic data provides abundant and perfect labels, their use has primarily been restricted to validating identical proteins or augmenting full-shot training, leaving the potential for few-shot adaptation to novel proteins unexplored. In this study, we investigate the effective utilization of synthetic data for few-shot particle picking. We identify that direct transfer fails due to a ``clean-vs-contaminated'' Sim2Real gap. To overcome this, we propose CryoAnomaly, which is a framework that turns this gap into an advantage. By employing an anomaly detector trained on clean synthetic data, we identify real-world contaminants as anomalies and suppress them via a novel anomaly-guided hard negative suppression loss. On the CryoPPP benchmark, CryoAnomaly achieves the best picking accuracy and reconstruction resolution among state-of-the-art methods in the few-shot setting. The proposed anomaly-guided loss is confirmed to be effective on datasets with diverse contamination, where reliable pseudo-anomaly masks can be generated. Our code, dataset, and project page are available at: https://github.com/riku359/CryoAnomaly, https://huggingface.co/datasets/rikrikrik/CryoAnomaly, https://riku359.github.io/CryoAnomaly-page/.
