Hostname: page-component-77c89778f8-9q27g Total loading time: 0 Render date: 2024-07-20T07:44:11.301Z Has data issue: false hasContentIssue false
  • English
  • Français

CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning.

Published online by Cambridge University Press:  30 July 2021

Ryan Conrad  and
Kedar Narayan
Ryan Conrad
Affiliation:
Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States
Kedar Narayan
Affiliation:
Frederick National Laboratory for Cancer Research, Frederick, Maryland, 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
From Images to Insights: Working with Large Multi-modal Data in Cell Biological Imaging
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 3036 - 3037
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Buhmann, J. et al. , “Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset,” bioRxiv, p. 2019.12.12.874172, Mar. 2019.Google Scholar
Lichtman, J. W., Pfister, H., and Shavit, N., “The big data challenges of connectomics,” Nat. Neurosci., vol. 17, no. 11, pp. 1448–1454, Oct. 2014.CrossRefGoogle ScholarPubMed
Conrad, R. and Narayan, K., “CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning,” bioRxiv. bioRxiv, p. 2020.12.11.421792, 11-Dec-2020.Google Scholar