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EPicker: An exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking

Computer Science

EPicker: An exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking

X. Zhang, T. Zhao, et al.

Discover how EPicker, developed by Xinyu Zhang and colleagues, revolutionizes cryo-electron microscopy particle picking by leveraging continual learning. This innovative approach not only enhances performance across new datasets but also prevents the loss of previously learned insights, allowing for improved identification of proteins, vesicles, and more.

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Playback language: English
Abstract
Deep learning methods are popular for cryo-electron microscopy (cryo-EM) particle picking, but their generalization to unseen datasets is unpredictable, and they often forget previously learned knowledge. This paper introduces EPicker, an exemplar-based continual learning approach that accumulates knowledge from new datasets without catastrophic forgetting. EPicker uses a dual-path network and a novel loss function to achieve this, enabling it to continuously improve particle picking performance and expand its ability to identify various biological objects (proteins, vesicles, fibers).
Publisher
Nature Communications
Published On
May 05, 2022
Authors
Xinyu Zhang, Tianfang Zhao, Jiansheng Chen, Yuan Shen, Xueming Li
Tags
deep learning
cryo-electron microscopy
particle picking
continual learning
biological objects
catastrophic forgetting
exemplar-based

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