Abstract
Over the past 2 years, we have witnessed the emergence of a number of deep learning-based methods for cryo-electron tomography (cryo-ET) data analysis. As a result of better instruments and data collection software in recent years, cryo-ET data are collected at a faster and faster pace, into the rim of high-throughput methods. However, cryo-ET data analysis tasks are still very challenging due to the low signal-to-noise ratio. The deep learning approach is very promising to the cryo-ET research community because of its scalability and potential to improve the accuracy with a large amount of data. In this survey, we review a number of deep learning-based methods proposed for cryo-ET data analysis tasks including segmentation, classification, and others. This survey also discusses their advantages and drawbacks, which provide potential directions for future research.
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