EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking

By Xinyu Zhang, Tianfang Zhao, Jiansheng Chen, Yuan Shen, Xueming Li. 2022

21 citations recorded by the source corpus.

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. Many existing deep learning algorithms for particle picking are not predictable on unseen datasets. Here the authors report an exemplar-based continual learning approach, EPicker, enabling accumulation of new knowledge of cryoEM particle picking without catastrophic forgetting of old knowledge.

Subfields: Media Technology, Structural Biology, Surfaces, Coatings and Films, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Media Technology

1. Jun Zhu
2. Hang Su

In Meta & Continual Learning

1. Xingxing Zhang
2. Liyuan Wang
3. Hang Su
4. Jun Zhu
5. Simone Scardapane
6. Valerio Marsocci
7. Liyuan Wang
8. Hongwei Yan

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “We present only some representative applications in the above two sections, with other more specialized and cross-cutting scenarios to be explored, such as robotics [25], [76], [243], graph learning [219], [423], [496], bioimaging [497], etc.”