UPicker: a semi-supervised particle picking transformer method for cryo-EM micrographs.

ABSTRACT: Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to o

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11631311 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature