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Automated Data Labeling and Label Cleaning for Nanoparticle Classification in Electron Microscopy

Published online by Cambridge University Press:  30 July 2021

Kate Groschner ,
Assaf Ben-Moshe ,
Alexander Pattinson ,
Wolfgang Theis  and
Mary Scott
Kate Groschner
Affiliation:
UC Berkeley, Oakland, California, United States
Assaf Ben-Moshe
Affiliation:
UC Berkeley, United States
Alexander Pattinson
Affiliation:
University of Birmingham, United States
Wolfgang Theis
Affiliation:
University of Birmingham, United States
Mary Scott
Affiliation:
UC Berkeley, Berkeley, California, 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. 2526 - 2528
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Wang, Y. et al. Symmetric cross entropy for robust learning with noisy labels. arXiv 322–330 (2019).Google Scholar
Rolnick, D., Veit, A., Belongie, S. & Shavit, N. Deep learning is robust to massive label noise. arXiv (2017).Google Scholar
Thulasidasan, S., Bhattacharya, T., Bilmes, J., Chennupati, G. & Mohd-Yusof, J. Combating label noise in deep learning using abstention. arXiv (2019).Google Scholar
Arazo, E., Ortego, D., Albert, P., O'Connor, N. E. & McGuinness, K. Unsupervised label noise modeling and loss correction. 36th Int. Conf. Mach. Learn. ICML 2019 2019–June, 465–474 (2019).Google Scholar
Han, B. et al. Co-teaching: Robust training of deep neural networks with extremely noisy labels. Adv. Neural Inf. Process. Syst. 2018–December, 8527–8537 (2018).Google Scholar
Karimi, D., Dou, H., Warfield, S. K. & Gholipour, A. Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. arXiv 1–22 (2019).Google Scholar
Hendrycks, D., Mazeika, M., Wilson, D. & Gimpel, K. Using trusted data to train deep networks on labels corrupted by severe noise. Adv. Neural Inf. Process. Syst. 2018–December, 10456–10465 (2018).Google Scholar
Ben-Moshe, A. et al. Enantioselective control of lattice and shape chirality in inorganic nanostructures using chiral biomolecules. Nat. Commun. 5 , (2014).CrossRefGoogle ScholarPubMed