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16 March 2023 Scalable and generalizable morphological profiling of cells by unsupervised deep learning (Conference Presentation)
Rashmi Sreeramachandra Murthy, Gwinky G. K. Yip, Anderson H. C. Shum, Kenneth K. Y. Wong, Kevin K. M. Tsia
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Proceedings Volume PC12390, High-Speed Biomedical Imaging and Spectroscopy VIII; PC1239005 (2023) https://doi.org/10.1117/12.2654627
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
We report an integrative unsupervised deep learning approach to translate the complex morphological information of cells into interpretable representations that can be generalizable for downstream single-cell analytics. The method, integrating the respective advantages of generative adversarial network and variational autoencoder, enables faithful prediction of biological processes based on cell morphology read out from different imaging modalities. We demonstrate the generalizability and scalability of this method in a diverse range of applications, including cellular responses to SARS-CoV-2 infection, cell-cycle progression imaged by high-throughput quantitative phase imaging (QPI), and cellular changes during epithelial to mesenchymal transition (EMT) captured by fluorescence imaging.
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Rashmi Sreeramachandra Murthy, Gwinky G. K. Yip, Anderson H. C. Shum, Kenneth K. Y. Wong, and Kevin K. M. Tsia "Scalable and generalizable morphological profiling of cells by unsupervised deep learning (Conference Presentation)", Proc. SPIE PC12390, High-Speed Biomedical Imaging and Spectroscopy VIII, PC1239005 (16 March 2023); https://doi.org/10.1117/12.2654627
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KEYWORDS
Data modeling
Profiling
Gallium nitride
Breast cancer
Tumor growth modeling
Image restoration
Information architecture
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