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Deep Learning Computer Vision for Anomaly Detection in Scanning Transmission Electron Microscopy

Published online by Cambridge University Press:  22 July 2022

Enea Prifti ,
Robert Klie  and
James Buban
Enea Prifti
Affiliation:
Physics Department, University of Illinois at Chicago, Chicago, IL, United States
Robert Klie
Affiliation:
Physics Department, University of Illinois at Chicago, Chicago, IL, United States
James Buban
Affiliation:
Physics Department, University of Illinois at Chicago, Chicago, IL, United States

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3018 - 3020
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Copyright
Copyright © Microscopy Society of America 2022

References

Sawada, H et al. , Journal of Electron Microscopy 58(6) (2009), p. 357. https://doi.org/10.1093/jmicro/dfp030Google Scholar
Kingma, DP and Welling, M, ArXiv.org (2014), https://arxiv.org/abs/1312.6114v10Google Scholar
This work was funded by a grant from the National Science Foundation (NSF DMR-1831406).Google Scholar