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We present a quantitative phase image (QPI) reconstruction method using generative deep learning (with high similarity of 91% and low error rate of < 1%), and its ability to integrate with a high-throughput microfluidic multimodal imaging flow cytometry platform (called multi-ATOM) that can consistently classify cancer cells in heterogeneous tumors from human non-small cell lung cancer patients at large scale (~200,000 cells) and high accuracy (~98%); and can reveal biophysical heterogeneity of tumors. This work represents another groundwork of synergizing high-throughput QPI and deep learning for future label-free intelligent clinical cancer diagnosis.
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Michelle C.K. Lo, Dickson M.D. Siu, Michael K. Y. Hsin, James C. M. Ho, Kevin K. Tsia, "Augmented quantitative phase imaging for large-scale label-free cancer-cell detection from heterogeneous tumors," Proc. SPIE PC11971, High-Speed Biomedical Imaging and Spectroscopy VII, PC119710D (2 March 2022); https://doi.org/10.1117/12.2608119