Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-27T01:47:47.140Z Has data issue: false hasContentIssue false

Learning Biology Through Puzzle-solving: Unbiased Automatic Understanding of Microscopy Images with Self-supervised Learning

Published online by Cambridge University Press:  30 July 2020

Alex Lu
Affiliation:
University of Toronto, Toronto, Ontario, Canada
Oren Kraus
Affiliation:
Phenomic AI, Toronto, Ontario, Canada
Sam Cooper
Affiliation:
Phenomic AI, Toronto, Ontario, Canada
Alan Moses
Affiliation:
University of Toronto, Toronto, Ontario, Canada

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Copyright
Copyright © Microscopy Society of America 2020

References

Uchida, S. Image processing and recognition for biological images. Dev. Growth Differ. 55, 523 (2013).10.1111/dgd.12054CrossRefGoogle ScholarPubMed
Bengio, Y., Courville, A. & Vincent, P. Representation Learning: A Review and New Perspectives. (2012).Google Scholar
Handfield, L.-F., Chong, Y. T., Simmons, J., Andrews, B. J. & Moses, A. M. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput. Biol. 9, e1003085 (2013).10.1371/journal.pcbi.1003085CrossRefGoogle ScholarPubMed
Li, Y., Majarian, T. D., Naik, A. W., Johnson, G. R. & Murphy, R. F. Point process models for localization and interdependence of punctate cellular structures. Cytom. Part A 89, 633643 (2016).10.1002/cyto.a.22873CrossRefGoogle ScholarPubMed
Kraus, O. Z. et al. Automated analysis of high-content microscopy data with deep learning. Mol. Syst. Biol. 13, (2017).10.15252/msb.20177551CrossRefGoogle ScholarPubMed
Sullivan, D. P. et al. Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat. Biotechnol. 36, 820828 (2018).10.1038/nbt.4225CrossRefGoogle ScholarPubMed
Jing, L. & Tian, Y. Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. (2019).Google Scholar
Lu, A. X., Kraus, O. Z., Cooper, S. & Moses, A. M. Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting. PLOS Comput. Biol. 15, e1007348 (2019).10.1371/journal.pcbi.1007348CrossRefGoogle ScholarPubMed
Chong, Y. T. et al. Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis. Cell 161, 14131424 (2015).10.1016/j.cell.2015.04.051CrossRefGoogle ScholarPubMed
Tkach, J. M. et al. Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nat. Cell Biol. 14, 966–76 (2012).10.1038/ncb2549CrossRefGoogle ScholarPubMed
Dubreuil, B. et al. YeastRGB: comparing the abundance and localization of yeast proteins across cells and libraries. Nucleic Acids Res. (2018) doi:10.1093/nar/gky941.Google Scholar
Thul, P. J. et al. A subcellular map of the human proteome. Science (80-.). 356, eaal3321 (2017).10.1126/science.aal3321CrossRefGoogle ScholarPubMed