Computational single-cell classification using deep learning on bright-field and phase images

Abstract

Automated cell classification is an important machine vision problem with significant benefits to biomedicine. We propose an efficient high-accuracy framework to classify cells based on bright-field and phase images using deep learning. With carefully designed network architecture and parameters, our network extracts features from single-cell images hierarchically and performs classification jointly. It can identify different types of cells without any human intervention and biological or hand-crafted features. Our experiments show that the system achieves a mean class accuracy of 96.5% on the single-cell images captured by an ultrafast time-stretch imager.