8. Deep Learning and Classification

As demonstrated in previous chapters, our TS-QPI system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. In this chapter, we use these biophysical measurements to form a hyperdimensional feature space in which supervised learning is performed for cell classification. We show that TS-QPI not only overcomes the throughput issue in cellular imaging, but also improves label-free diagnosis by integration of sensing multiple biophysical features. We also compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.