title: Deep Learning in Label-free Cell Classification.
abstract: 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. Here, we 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. Our 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. We 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.
authors.affiliation: Department of Electrical Engineering, University of California, Los Angeles, California 90095, USA.|California NanoSystems Institute, Los Angeles, California 90095, USA.
authors.forename: Claire Lifan
authors.initials: CL
authors.lastname: Chen
authors.affiliation: Department of Electrical Engineering, University of California, Los Angeles, California 90095, USA.|California NanoSystems Institute, Los Angeles, California 90095, USA.
authors.forename: Ata
authors.initials: A
authors.lastname: Mahjoubfar
authors.affiliation: California NanoSystems Institute, Los Angeles, California 90095, USA.
authors.forename: Li-Chia
authors.initials: LC
authors.lastname: Tai
authors.affiliation: Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095.
authors.forename: Ian K
authors.initials: IK
authors.lastname: Blaby
authors.affiliation: Department of Electrical Engineering, University of California, Los Angeles, California 90095, USA.
authors.forename: Allen
authors.initials: A
authors.lastname: Huang
authors.affiliation: California NanoSystems Institute, Los Angeles, California 90095, USA.|NantWorks, LLC, Culver City, California 90232, USA.|Department of Bioengineering, University of California, Los Angeles, California 90095, USA.
authors.forename: Kayvan Reza
authors.initials: KR
authors.lastname: Niazi
authors.affiliation: Department of Electrical Engineering, University of California, Los Angeles, California 90095, USA.|California NanoSystems Institute, Los Angeles, California 90095, USA.|Department of Bioengineering, University of California, Los Angeles, California 90095, USA.|Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA.
authors.forename: Bahram
authors.initials: B
authors.lastname: Jalali
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D045744:Cell Line, Tumor / Q000145:classification / Q000473:pathology; D016825:Chlamydomonas reinhardtii / Q000145:classification / Q000166:cytology / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012986:Software Validation; D060388:Support Vector Machine; D013601:T-Lymphocytes / Q000145:classification / Q000166:cytology