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Benchmark tests of atom-locating CNN models with a consistent dataset

Published online by Cambridge University Press:  30 July 2021

Jingrui Wei ,
Ben Blaiszik ,
Dane Morgan  and
Paul Voyles
Jingrui Wei
Affiliation:
Department of Materials Science and Engineering, University of Wisconsin Madison, Madison, Wisconsin, United States
Ben Blaiszik
Affiliation:
Globus, University of Chicago, United States
Dane Morgan
Affiliation:
University of Wisconsin Madison, United States
Paul Voyles
Affiliation:
University of Wisconsin Madison, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 2518 - 2520
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Ge, M., & Xin, H. L. Deep Learning Based Atom Segmentation and Noise and Missing-Wedge Reduction for Electron Tomography. Microscopy and Microanalysis, 24(S1), 504–505 (2018).CrossRefGoogle Scholar
Ziatdinov, M., Dyck, O., Kalinin, S. V., et al. Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. ACS Nano, 11(12), 12742–12752(2017).CrossRefGoogle ScholarPubMed
Badrinarayanan, V., Kendall, A., & Cipolla, R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495 (2017).CrossRefGoogle ScholarPubMed