Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-07-04T03:12:50.840Z Has data issue: false hasContentIssue false
  • English
  • Français

Rapid and Flexible Few Shot Learning-Based Classification of Scanning Transmission Electron Microscopy Data

Published online by Cambridge University Press:  30 July 2021

Sarah Reehl ,
Elizabeth Kautz ,
Matthew Olszta ,
Derek Hopkins ,
Bethany Matthews ,
Le Wang ,
Yingge Du  and
Steven Spurgeon
Sarah Reehl
Affiliation:
Pacific Northwest National Laboratory, United States
Elizabeth Kautz
Affiliation:
Pacific Northwest National Laboratory - PNNL, Richland, Washington, United States
Matthew Olszta
Affiliation:
Pacific Northwest National Laboratory, Washington, United States
Derek Hopkins
Affiliation:
Pacific Northwest National Laboratory, United States
Bethany Matthews
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, United States
Le Wang
Affiliation:
Pacific Northwest National Laboratory, United States
Yingge Du
Affiliation:
Pacific Northwest National Laboratory, United States
Steven Spurgeon
Affiliation:
Pacific Northwest National Laboratory, United States

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
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. 1618 - 1619
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America