Global Training Of Neural Networks For Phenomic Classification

Summary

UCLA researchers in the Department of Electrical Engineering have developed a high-throughput, label-free cell classification method based on time-stretch quantitative phase imaging.

Background

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput.

Innovation

UCLA researchers integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. The system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Applications

• Cell screening and classification
• Medical, biotechnological and research application

Advantages

• Label free
• High throughput
• High resolution
• High accuracy

Patent Status

Related Materials

• Chen, C. L., Mahjoubfar, A., Tai, L. C., Blaby, I. K., Huang, A., Niazi, K. R., & Jalali, B. (2016). Deep learning in label-free cell classification. Scientific reports, 6, 21471.