S. Alizada

Temporal dynamics improves machine learning-based prediction of cell state from quantitative phase imaging

The results show that dynamic information, rather than static morphology alone, drives accurate cell-state classification, and that 3D architectures are needed to fully exploit it for label-free dynamic phenotyping.