  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%.
