Large-scale, batch-effect-free augmented quantitative phase imaging by generative learning

Michelle C. K. Lo; Dickson M. D. Siu; Kevin K. Tsia

doi:10.1117/12.2649822

16 March 2023 Large-scale, batch-effect-free augmented quantitative phase imaging by generative learning

Michelle C. K. Lo, Dickson M. D. Siu, Kevin K. Tsia

Author Affiliations +

Proceedings Volume 12390, High-Speed Biomedical Imaging and Spectroscopy VIII; 1239005 (2023) https://doi.org/10.1117/12.2649822
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

We present an unprecedented, generative deep learning model (named beGAN) in reconstructing batch-effect-free quantitative phase image (QPI). By employing the high-throughput microfluidic multimodal imaging flow cytometry platform (i.e. multi-ATOM), our model demonstrated a robust QPI prediction from brightfield on various lung cancer cell lines (>800,000 cells). With batch-free QPI, biophysical phenotypes of cells are unified across batches and a significant improvement from 33.61% to 91.34% is achieved on the cross-batches cancer cell lines classification. This work unveil an avenue on overcoming batch effect with deep learning at single-cell imaging level.

Conference Presentation

Citation Download Citation

Michelle C. K. Lo, Dickson M. D. Siu, and Kevin K. Tsia "Large-scale, batch-effect-free augmented quantitative phase imaging by generative learning", Proc. SPIE 12390, High-Speed Biomedical Imaging and Spectroscopy VIII, 1239005 (16 March 2023); https://doi.org/10.1117/12.2649822

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