Abstract
Deep learning is a popular method for facilitating particle picking in single-particle cryo-electron microscopy (cryo-EM), which is essential for developing automated processing pipelines. Most existing deep learning algorithms for particle picking rely on supervised learning where the features to be identified must be provided through a training procedure. However, the generalization performance of these algorithms on unseen datasets with different features is often unpredictable. In addition, while they perform well on the latest training datasets, these algorithms often fail to maintain the knowledge of old particles. Here, we report an exemplar-based continual learning approach, which can accumulate knowledge from the new dataset into the model by training an existing model on only a few new samples without catastrophic forgetting of old knowledge, implemented in a program called EPicker. Therefore, the ability of EPicker to identify bio-macromolecules can be expanded by continuously learning new knowledge during routine particle picking applications. Powered by the improved training strategy, EPicker is designed to pick not only protein particles but also general biological objects such as vesicles and fibers.