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

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.