Deep Learning in Label-free Cell Classification

By Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian Blaby, A. Huang, Kayvan Reza Niazi, Bahram Jalali. Scientific Reports, 2016

432 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/0a4804b7bbc943073c3fa7693035be7fd017e324

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Citations in the corpus

Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search (2020, background): “While classical vision techniques suffer from issues like poorgeneralization, image cytometry is limited by its operational speed and inability to engineer complex features [7].”
Deep learning in bioinformatics (2016, background): “Protein classification [108] Recognition [142, 143]”
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Deep Learning for Imaging Flow Cytometry: (2016, background): “73 Other recent work on deep learning in high-throughput microscopy either relied on 74 engineered features [8], focused on whole-image segmentation without addressing 75 visualization of network features [9].”
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Machine learning and applications in ultrafast photonics (2020, background): “classification [12], super resolution microscopy [13, 14], quantum optics [15], and optical 39 communications [16–18].”