Applications of deep learning in electron microscopy

被引：37

作者：

Treder, Kevin P. ^{[1
]}

Huang, Chen ^{[2
]}

Kim, Judy S. ^{[1
,2
]}

Kirkland, Angus, I ^{[1
,2
]}

机构：

[1] Univ Oxford, Dept Mat, Oxford OX1 3PH, Oxon, England

[2] Rosalind Franklin Inst, Harwell Res Campus, Didcot OX11 0FA, Oxon, England

来源：

MICROSCOPY | 2022年 / 71卷

基金：

英国工程与自然科学研究理事会;

关键词：

artificial intelligence; machine learning; deep learning; electron microscopy; neural networks; cryo-EM; AUTOMATIC PARTICLE PICKUP; CONVOLUTIONAL NEURAL-NETWORKS; CRYO-EM; SEMANTIC SEGMENTATION; RECONSTRUCTION; IMAGES; CLASSIFICATION; RECOGNITION; ALGORITHMS; SIZE;

D O I：

10.1093/jmicro/dfab043

中图分类号：

TH742 [显微镜];

学科分类号：

摘要：

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.

引用

页码：i100 / i115

页数：16

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