Difference between revisions of "2022Treder DL"
(Created page with "== Citation == Treder, Kevin P. / Huang, Chen / Kim, Judy S. / Kirkland, Angus I. Applications of deep learning in electron microscopy. 2022-02, Microscopy, Vol. 71, p. i100-...") |
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Latest revision as of 10:17, 7 April 2022
Citation
Treder, Kevin P. / Huang, Chen / Kim, Judy S. / Kirkland, Angus I. Applications of deep learning in electron microscopy. 2022-02, Microscopy, Vol. 71, p. i100-i115
Abstract
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.
Keywords
Links
https://academic.oup.com/jmicro/article/71/Supplement_1/i100/6530481