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使用三重损失嵌入的冷电磁显微镜的少数镜头分类

Alexander Ho^1,2, Brandon Sung³, Sukyeong Lee^1,2

¹Advanced Technology Core for Macromolecular X-Ray Crystallography, Baylor College of Medicine, Houston, TX 77030.

bioRxiv : the preprint server for biology

|November 19, 2025

概括

这项研究引入了一种新的几次射击学习方法,用于评估冷电子显微镜 (cryo-EM) 显微镜的质量. 这种方法有效地用最小的标记数据对微图进行分类,改进了自动选过程.

科学领域:

结构生物学结构生物学
生物物理学的生物物理.
计算生物学计算生物学

背景情况:

低温电子显微镜 (cryo-EM) 显微镜的手动选是耗时且计算成本昂贵的.
自动化质量评估对于高效的冷EM数据处理至关重要.
低射线学习在冷EM中的应用正在出现,但在微图级质量评估方面尚未得到充分探索.

研究的目的:

开发和评估用于自动化冷电磁显微镜质量评估的一些射击学习框架.
提高冷EM数据处理的效率并减少手动标签负担.
探索三重损失嵌入的有效性,用于微图分类.

主要方法:

开发了一个框架,将少数射击学习与冷-EM微图分类结合起来.
使用三重损失嵌入,整合实空间和里埃空间信息.
该模型在多个EMPIAR数据集上进行了训练和验证.

主要成果:

提出的方法在微图分类中取得了竞争性表现,每类仅有1-5个标记的示例.
三倍损失嵌入方法在少数拍摄场景中表现优于传统的交叉训练.
该框架在不同的EMPIAR数据集中表现出强度.

结论:

短期学习为快速适应冷EM中自动化微图查提供了一种实际解决方案.
开发的框架大大减少了大量手动标签的需要.
这种方法提高了冷电磁数据处理管道的效率.

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