CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders

Chentianye Xu, Xueying Zhan, Min Xu; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3876-3885

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%. Our code is available at: https://github.com/xulabs/aitom.