期刊
MICROSCOPY
卷 71, 期 -, 页码 i100-i115出版社
OXFORD UNIV PRESS
DOI: 10.1093/jmicro/dfab043
关键词
artificial intelligence; machine learning; deep learning; electron microscopy; neural networks; cryo-EM
类别
资金
- EPSRC [EP/T033452/1, EP/S001999/1]
- Johnson Matthey plc, iCASE award [2113841]
- Rosalind Franklin Institute
This article reviews the growing use of machine learning in electron microscopy, covering various network architectures and error metrics applied to EM-related problems. It also highlights the application of these methods in both physical and life sciences, specifically tailored for EM.
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM.
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