Abstract
Cryo-electron microscopy (cryo-EM) emerges as a pivotal technology for determining the architecture of cells, viruses, and protein assemblies at near-atomic resolution. Traditional particle picking, a key step in cryo-EM, struggles with manual effort and automated methods' sensitivity to low signal-to-noise ratio (SNR) and varied particle orientations. Furthermore, existing neural network (NN)-based approaches often require extensive labeled datasets, limiting their practicality. To overcome these obstacles, we introduce cryoMAE, a novel approach based on few-shot learning that harnesses the capabilities of Masked Autoencoders (MAE) to enable efficient selection of single particles in cryo-EM images. Contrary to conventional NN-based techniques, cryoMAE requires only a minimal set of positive particle images for training yet demonstrates high performance in particle detection. Furthermore, the implementation of a self-cross similarity loss ensures distinct features for particle and background regions, thereby enhancing the discrimination capability of cryoMAE. Experiments on large-scale cryo-EM datasets show that cryoMAE outperforms existing state-of-the-art (SOTA) methods, improving 3D reconstruction resolution by up to 22.4%.
Abstract (translated)
冷冻电镜（冷冻EM）作为一种在近原子分辨率下确定细胞、病毒和蛋白质装配体架构的关键技术而脱颖而出。传统的颗粒选择步骤，在冷冻EM中是一个关键步骤，但是却面临着手动努力和自动方法对低信号噪声比（SNR）和不同颗粒取向的敏感性。此外，现有的基于神经网络（NN）的方法通常需要大量带标签的数据集，限制了其实用性。为了克服这些障碍，我们引入了冷冻MAE，一种基于少样本学习的新方法，利用了掩码自动编码器（MAE）的特性，实现了在冷冻EM图像中高效选择单个颗粒。 与传统NN-based方法不同，冷冻MAE只需要一个最小的带正粒子图像的训练集，但在颗粒检测方面表现出高效。此外，自监督损失函数的实现确保了粒子和水印区域之间的明显差异，从而提高了冷冻MAE的识别能力。在大型冷冻EM数据集的实验中，冷冻MAE超越了现有最先进的（SOTA）方法，通过提高3D重建分辨率高达22.4%而表现出色。
URL
https://arxiv.org/abs/2404.10178
