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Review : Deep learning in electron microscopy

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Ede, Jeffrey M. (2020) Review : Deep learning in electron microscopy. Working Paper. Cornell University: arXiv. (Unpublished)

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Official URL: https://arxiv.org/abs/2009.08328v2

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Abstract

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

Item Type: Working or Discussion Paper (Working Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QC Physics
Divisions: Faculty of Science > Physics
Library of Congress Subject Headings (LCSH): Machine learning, Electron microscopy , Electron microscopy -- Data processing
Publisher: arXiv
Place of Publication: Cornell University
Official Date: 18 September 2020
Dates:
DateEvent
18 September 2020Available
Number of Pages: 97
Institution: University of Warwick
Status: Not Peer Reviewed
Publication Status: Unpublished
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/N035437/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
Studentship 1917382[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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