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Quantifying Differences Between Machine Learning Classification Models Applied to Cancer Microscopy Data

Published online by Cambridge University Press:  22 July 2022

William Franz Lamberti  and
Chongzhi Zang
William Franz Lamberti
Affiliation:
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
Chongzhi Zang*
Affiliation:
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
*
*Corresponding author: zang@virginia.edu

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. 3006 - 3008
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Copyright
Copyright © Microscopy Society of America 2022

References

von Chamier, L., et al. , “Democratising deep learning for microscopy with ZeroCostDL4Mic,” Nature Communications, vol. 12, no. 1, p. 2276, Apr. 2021. [Online]. Available: https://www.nature.com/articles/s41467-021-22518-0CrossRefGoogle ScholarPubMed
Waller, L. and Tian, L., “Machine learning for 3D microscopy,” Nature, vol. 523, no. 7561, pp. 416–417, Jul. 2015. [Online]. Available: http://www.nature.com/articles/523416aCrossRefGoogle ScholarPubMed
Zinchuk, V. and Grossenbacher-Zinchuk, O., “Machine Learning for Analysis of Microscopy Images: A Practical Guide,” Current Protocols in Cell Biology, vol. 86, no. 1, p. e101, 2020. [Online]. Available:https://onlinelibrary.wiley.com/doi/abs/10.1002/cpcb.101CrossRefGoogle ScholarPubMed
Labati, R. D., Piuri, V., and Scotti, F., “All-IDB: The acute lymphoblastic leukemia image database for image processing,” in 2011 18th IEEE International Conference on Image Processing. Brussels, Belgium: IEEE, Sep.2011, pp. 2045–2048. [Online]. Available: http://ieeexplore.ieee.org/document/6115881/ 10.1109/ICIP.2011.6115881CrossRefGoogle Scholar
Gupta, A. and Gupta, R., Eds., ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging: Select Proceedings, ser. Lecture Notes in Bioengineering. Springer Singapore, 2019. [Online]. Available: https://www.springer.com/gp/book/9789811507977CrossRefGoogle Scholar
Cremer, M., et al. , “Cohesin depleted cells rebuild functional nuclear compartments after endomitosis,” Nature Communications, vol. 11, no. 1, p. 6146, Dec. 2020. [Online]. Available: https://www.nature.com/articles/s41467-020-19876-6CrossRefGoogle ScholarPubMed
James, G., Witten, D., Hastie, T., and Tibshirani, R., Eds., An introduction to statistical learning: with applications in R, ser. Springer texts in statistics. New York: Springer, 2013, no. 103, oCLC: ocn828488009.CrossRefGoogle Scholar