Deep Learning in Label-free Cell Classification

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

doi:10.1038/srep21471

Title: Deep Learning in Label-free Cell Classification

Journal Article · Tue Mar 15 00:00:00 EDT 2016 · Scientific Reports

DOI:https://doi.org/10.1038/srep21471· OSTI ID:1310540

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

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. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

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Research Organization:: Univ. of California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FC02-02ER63421

OSTI ID:: 1310540

Journal Information:: Scientific Reports, Vol. 6; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 216 works

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References (64)

Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Chlamydomonas starchless mutant defective in ADP-glucose pyrophosphorylase hyper-accumulates triacylglycerol Li, Yantao; Han, Danxiang; Hu, Guongrong Metabolic Engineering, Vol. 12, Issue 4, p. 387-391 https://doi.org/10.1016/j.ymben.2010.02.002	journal	July 2010
CellProfiler: image analysis software for identifying and quantifying cell phenotypes Carpenter, Anne E.; Jones, Thouis R.; Lamprecht, Michael R. Genome Biology, Vol. 7, Issue 10 https://doi.org/10.1186/gb-2006-7-10-r100	journal	October 2006
Labeling Human Mesenchymal Stem Cells with Fluorescent Contrast Agents: the Biological Impact Boddington, Sophie E.; Sutton, Elizabeth J.; Henning, Tobias D. Molecular Imaging and Biology, Vol. 13, Issue 1 https://doi.org/10.1007/s11307-010-0322-0	journal	April 2010
Using AUC and accuracy in evaluating learning algorithms IEEE Transactions on Knowledge and Data Engineering, Vol. 17, Issue 3 https://doi.org/10.1109/TKDE.2005.50	journal	March 2005
Deep Learning Ganatra, Sanket Zenodo https://doi.org/10.5281/zenodo.1189359	text	January 2018
Hydrodynamic Focusing on a Silicon Chip: Mixing Nanoliters in Microseconds Knight, James B.; Vishwanath, Ashvin; Brody, James P. Physical Review Letters, Vol. 80, Issue 17 https://doi.org/10.1103/PhysRevLett.80.3863	journal	April 1998
High-throughput single-microparticle imaging flow analyzer Goda, K.; Ayazi, A.; Gossett, D. R. Proceedings of the National Academy of Sciences, Vol. 109, Issue 29 https://doi.org/10.1073/pnas.1204718109	journal	July 2012
Optical phase recovery in the dispersive Fourier transform Solli, D. R.; Gupta, S.; Jalali, B. Applied Physics Letters, Vol. 95, Issue 23 https://doi.org/10.1063/1.3271678	journal	December 2009
Variability and memory of protein levels in human cells Sigal, Alex; Milo, Ron; Cohen, Ariel Nature, Vol. 444, Issue 7119 https://doi.org/10.1038/nature05316	journal	November 2006
Effect of focus on cell detection and recognition by the cell analyzer Spadinger, Ingrid; Poon, Steven S. S.; Palcic, Branko Cytometry, Vol. 11, Issue 4 https://doi.org/10.1002/cyto.990110403	journal	January 1990
Starchless Mutants of Chlamydomonas reinhardtii Lack the Small Subunit of a Heterotetrameric ADP-Glucose Pyrophosphorylase Zabawinski, C.; Van Den Koornhuyse, N.; D'Hulst, C. Journal of Bacteriology, Vol. 183, Issue 3 https://doi.org/10.1128/JB.183.3.1069-1077.2001	journal	February 2001
Beyond counting tumor cells Kling, Jim Nature Biotechnology, Vol. 30, Issue 7 https://doi.org/10.1038/nbt.2295	journal	July 2012
Stochasticity in gene expression: from theories to phenotypes Kærn, Mads; Elston, Timothy C.; Blake, William J. Nature Reviews Genetics, Vol. 6, Issue 6 https://doi.org/10.1038/nrg1615	journal	May 2005
Design of Warped Stretch Transform Mahjoubfar, Ata; Chen, Claire Lifan; Jalali, Bahram Scientific Reports, Vol. 5, Issue 1 https://doi.org/10.1038/srep17148	journal	November 2015
Random forest-based prediction of stroke outcome Fernandez-Lozano, Carlos; Hervella, Pablo; Mato-Abad, Virginia Scientific Reports, Vol. 11, Issue 1 https://doi.org/10.1038/s41598-021-89434-7	journal	May 2021
Coherent time-stretch transformation for real-time capture of wideband signals Buckley, Brandon W.; Madni, Asad M.; Jalali, Bahram Optics Express, Vol. 21, Issue 18 https://doi.org/10.1364/OE.21.021618	journal	January 2013
Isolation by Size of Epithelial Tumor Cells Vona, Giovanna; Sabile, Abdelmajid; Louha, Malek The American Journal of Pathology, Vol. 156, Issue 1 https://doi.org/10.1016/S0002-9440(10)64706-2	journal	January 2000
Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena Goda, K.; Tsia, K. K.; Jalali, B. Nature, Vol. 458, Issue 7242 https://doi.org/10.1038/nature07980	journal	April 2009
Future possibilities for artificial intelligence in the practical management of hypertension Koshimizu, Hiroshi; Kojima, Ryosuke; Okuno, Yasushi Hypertension Research, Vol. 43, Issue 12 https://doi.org/10.1038/s41440-020-0498-x	journal	July 2020
On the abundance of EpCAM on cancer stem cells Gires, Olivier; Klein, Christoph A.; Baeuerle, Patrick A. Nature Reviews Cancer, Vol. 9, Issue 2 https://doi.org/10.1038/nrc2499-c1	journal	January 2009
Increased Lipid Accumulation in the Chlamydomonas reinhardtii sta7-10 Starchless Isoamylase Mutant and Increased Carbohydrate Synthesis in Complemented Strains Work, Victoria H.; Radakovits, Randor; Jinkerson, Robert E. Eukaryotic Cell, Vol. 9, Issue 8 https://doi.org/10.1128/EC.00075-10	journal	June 2010
Real time blood testing using quantitative phase imaging Pham, Hoa V.; Bhaduri, Basanta; Tangella, Krishnarao Digital Holography and Three-Dimensional Imaging https://doi.org/10.1364/dh.2013.dm4a.4	conference	January 2013
Cellular Image Analysis and Imaging by Flow Cytometry Basiji, David A.; Ortyn, William E.; Liang, Luchuan Clinics in Laboratory Medicine, Vol. 27, Issue 3 https://doi.org/10.1016/j.cll.2007.05.008	journal	September 2007
Dispersive Fourier transformation for fast continuous single-shot measurements Goda, K.; Jalali, B. Nature Photonics, Vol. 7, Issue 2 https://doi.org/10.1038/nphoton.2012.359	journal	January 2013
TAG, You’re it! Chlamydomonas as a reference organism for understanding algal triacylglycerol accumulation Merchant, Sabeeha S.; Kropat, Janette; Liu, Bensheng Current Opinion in Biotechnology, Vol. 23, Issue 3 https://doi.org/10.1016/j.copbio.2011.12.001	journal	June 2012
Label-free high-throughput imaging flow cytometry Mahjoubfar, A.; Chen, C.; Niazi, K. R. SPIE LASE, SPIE Proceedings https://doi.org/10.1117/12.2040881	conference	March 2014
Hilbert phase microscopy for investigating fast dynamics in transparent systems Ikeda, Takahiro; Popescu, Gabriel; Dasari, Ramachandra R. Optics Letters, Vol. 30, Issue 10 https://doi.org/10.1364/OL.30.001165	journal	January 2005
The hydrodynamic focusing effect inside rectangular microchannels Lee, Gwo-Bin; Chang, Chih-Chang; Huang, Sung-Bin Journal of Micromechanics and Microengineering, Vol. 16, Issue 5 https://doi.org/10.1088/0960-1317/16/5/020	journal	April 2006
Neural network analysis of flow cytometric data for 40 marine phytoplankton species Boddy, Lynne; Morris, C. W.; Wilkins, M. F. Cytometry, Vol. 15, Issue 4 https://doi.org/10.1002/cyto.990150403	journal	April 1994
Living with Noisy Genes: How Cells Function Reliably with Inherent Variability in Gene Expression Maheshri, Narendra; O’Shea, Erin K. Annual Review of Biophysics and Biomolecular Structure, Vol. 36, Issue 1 https://doi.org/10.1146/annurev.biophys.36.040306.132705	journal	June 2007
Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis Spencer, Sabrina L.; Gaudet, Suzanne; Albeck, John G. Nature, Vol. 459, Issue 7245 https://doi.org/10.1038/nature08012	journal	April 2009
The Path to Triacylglyceride Obesity in the sta6 Strain of Chlamydomonas reinhardtii Goodenough, Ursula; Blaby, Ian; Casero, David Eukaryotic Cell, Vol. 13, Issue 5 https://doi.org/10.1128/EC.00013-14	journal	February 2014
Seventeen-colour flow cytometry: unravelling the immune system Perfetto, Stephen P.; Chattopadhyay, Pratip K.; Roederer, Mario Nature Reviews Immunology, Vol. 4, Issue 8 https://doi.org/10.1038/nri1416	journal	August 2004
Hilbert transforms King, Frederick W. https://doi.org/10.1017/CBO9780511721458	book	January 2009
Deep Learning in Neural Networks: An Overview Schmidhuber, Juergen arXiv https://doi.org/10.48550/arxiv.1404.7828	text	January 2014
Adhesion of culture cells to their substratum Revel, J. P.; Hoch, Pamela; Ho, D. Experimental Cell Research, Vol. 84, Issue 1-2 https://doi.org/10.1016/0014-4827(74)90398-X	journal	March 1974
Refractometry of Living Cells Barer, R.; Ross, K. F. A.; Tkaczyk, S. Nature, Vol. 171, Issue 4356 https://doi.org/10.1038/171720a0	journal	April 1953
The meaning and use of the area under a receiver operating characteristic (ROC) curve. Hanley, J. A.; McNeil, B. J. Radiology, Vol. 143, Issue 1 https://doi.org/10.1148/radiology.143.1.7063747	journal	April 1982
Live Cell Interferometry Quantifies Dynamics of Biomass Partitioning during Cytokinesis Zangle, Thomas A.; Teitell, Michael A.; Reed, Jason PLoS ONE, Vol. 9, Issue 12 https://doi.org/10.1371/journal.pone.0115726	journal	December 2014
Optical Properties of Circulating Human Blood in the Wavelength Range 400–2500 nm Roggan, André; Friebel, Moritz; Dörschel, Klaus Journal of Biomedical Optics, Vol. 4, Issue 1 https://doi.org/10.1117/1.429919	journal	January 1999
Mitochondrial Variability as a Source of Extrinsic Cellular Noise Johnston, Iain G.; Gaal, Bernadett; Neves, Ricardo Pires das PLoS Computational Biology, Vol. 8, Issue 3 https://doi.org/10.1371/journal.pcbi.1002416	journal	March 2012
Optically amplified detection for biomedical sensing and imaging Mahjoubfar, Ata; Goda, Keisuke; Betts, Gary Journal of the Optical Society of America A, Vol. 30, Issue 10 https://doi.org/10.1364/JOSAA.30.002124	journal	January 2013
Variability and Robustness in T Cell Activation from Regulated Heterogeneity in Protein Levels Feinerman, O.; Veiga, J.; Dorfman, J. R. Science, Vol. 321, Issue 5892 https://doi.org/10.1126/science.1158013	journal	August 2008
Automated image analysis of nuclear shape: What can we learn from a prematurely aged cell? Driscoll, Meghan K.; Albanese, Jason L.; Xiong, Zheng-Mei Aging, Vol. 4, Issue 2 https://doi.org/10.18632/aging.100434	journal	February 2012
High content screening: seeing is believing Zanella, Fabian; Lorens, James B.; Link, Wolfgang Trends in Biotechnology, Vol. 28, Issue 5 https://doi.org/10.1016/j.tibtech.2010.02.005	journal	May 2010
Ultrafast Three-Dimensional Surface Imaging Based on Short-Time Fourier Transform Xing, Fangjian; Chen, Hongwei; Xie, Shizhong IEEE Photonics Technology Letters, Vol. 27, Issue 21 https://doi.org/10.1109/LPT.2015.2461442	journal	November 2015
Systems-Level Analysis of Nitrogen Starvation-Induced Modifications of Carbon Metabolism in a Chlamydomonas reinhardtii Starchless Mutant Blaby, I. K.; Glaesener, A. G.; Mettler, T. The Plant Cell, Vol. 25, Issue 11 https://doi.org/10.1105/tpc.113.117580	journal	November 2013
ROC-Based Utility Function Maximization for Feature Selection and Classification with Applications to High-Dimensional Protease Data Liu, Zhenqiu; Tan, Ming Biometrics, Vol. 64, Issue 4 https://doi.org/10.1111/j.1541-0420.2008.01015.x	journal	March 2008
28 MHz swept source at 10 μm for ultrafast quantitative phase imaging Wei, Xiaoming; Lau, Andy K. S.; Xu, Yiqing Biomedical Optics Express, Vol. 6, Issue 10 https://doi.org/10.1364/BOE.6.003855	journal	January 2015
Highly Efficient Circulating Tumor Cell Isolation from Whole Blood and Label-Free Enumeration Using Polymer-Based Microfluidics with an Integrated Conductivity Sensor Adams, André A.; Okagbare, Paul I.; Feng, Juan Journal of the American Chemical Society, Vol. 130, Issue 27 https://doi.org/10.1021/ja8015022	journal	July 2008
Isolation of rare circulating tumour cells in cancer patients by microchip technology Nagrath, Sunitha; Sequist, Lecia V.; Maheswaran, Shyamala Nature, Vol. 450, Issue 7173 https://doi.org/10.1038/nature06385	journal	December 2007
Coherent Time-Stretch Transform for Near-Field Spectroscopy DeVore, Peter T. S.; Buckley, Brandon W.; Asghari, Mohammad H. IEEE Photonics Journal, Vol. 6, Issue 2 https://doi.org/10.1109/JPHOT.2014.2312949	journal	April 2014
Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software Kamentsky, Lee; Jones, Thouis R.; Fraser, Adam Bioinformatics, Vol. 27, Issue 8 https://doi.org/10.1093/bioinformatics/btr095	journal	February 2011
Optical Data Compression in Time Stretch Imaging Chen, Claire Lifan; Mahjoubfar, Ata; Jalali, Bahram PLOS ONE, Vol. 10, Issue 4 https://doi.org/10.1371/journal.pone.0125106	journal	April 2015
The use of the area under the ROC curve in the evaluation of machine learning algorithms Bradley, Andrew P. Pattern Recognition, Vol. 30, Issue 7, p. 1145-1159 https://doi.org/10.1016/S0031-3203(96)00142-2	journal	July 1997
Real Time Blood Testing Using Quantitative Phase Imaging Pham, Hoa V.; Bhaduri, Basanta; Tangella, Krishnarao PLoS ONE, Vol. 8, Issue 2 https://doi.org/10.1371/journal.pone.0055676	journal	February 2013
Label-free cell separation and sorting in microfluidic systems Gossett, Daniel R.; Weaver, Westbrook M.; Mach, Albert J. Analytical and Bioanalytical Chemistry, Vol. 397, Issue 8 https://doi.org/10.1007/s00216-010-3721-9	journal	April 2010
Theory of amplified dispersive Fourier transformation Goda, Keisuke; Solli, Daniel R.; Tsia, Kevin K. Physical Review A, Vol. 80, Issue 4 https://doi.org/10.1103/PhysRevA.80.043821	journal	October 2009
Deep learning in neural networks: An overview Schmidhuber, Jürgen Neural Networks, Vol. 61 https://doi.org/10.1016/j.neunet.2014.09.003	journal	January 2015
Hilbert transforms King, Frederick W. https://doi.org/10.1017/CBO9780511735271	journal	January 2009
Substrate retention of fractured retraction fibres during detachment of trypsinized BHK21 fibroblasts Whur, P.; Koppel, H.; Urquhart, C. M. Journal of Cell Science, Vol. 24, Issue 1 https://doi.org/10.1242/jcs.24.1.265	journal	April 1977
Tailoring Wideband Signals With a Photonic Hardware Accelerator Jalali, Bahram; Mahjoubfar, Ata Proceedings of the IEEE, Vol. 103, Issue 7 https://doi.org/10.1109/jproc.2015.2418538	journal	July 2015
Label-free high-throughput cell screening in flow Mahjoubfar, Ata; Chen, Claire; Niazi, Kayvan R. Biomedical Optics Express, Vol. 4, Issue 9 https://doi.org/10.1364/boe.4.001618	journal	January 2013

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Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip Heo, Young Jin; Lee, Donghyeon; Kang, Junsu Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-11534-0	journal	September 2017
Predicting cancer cell invasion by single-cell physical phenotyping Nyberg, Kendra D.; Bruce, Samuel L.; Nguyen, Angelyn V. Integrative Biology, Vol. 10, Issue 4 https://doi.org/10.1039/c7ib00222j	journal	March 2018
Quantitative Phase Imaging Flow Cytometry for Ultra‐Large‐Scale Single‐Cell Biophysical Phenotyping Lee, Kelvin C. M.; Wang, Maolin; Cheah, Kathryn S. E. Cytometry Part A, Vol. 95, Issue 5 https://doi.org/10.1002/cyto.a.23765	journal	April 2019
Machine Learning Based Real‐Time Image‐Guided Cell Sorting and Classification Gu, Yi; Zhang, Alex Ce; Han, Yuanyuan Cytometry Part A, Vol. 95, Issue 5 https://doi.org/10.1002/cyto.a.23764	journal	April 2019
The Next Era: Deep Learning in Pharmaceutical Research Ekins, Sean Pharmaceutical Research, Vol. 33, Issue 11 https://doi.org/10.1007/s11095-016-2029-7	journal	September 2016
Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis Pu, Guoqing; Yi, Lilin; Zhang, Li Light: Science & Applications, Vol. 9, Issue 1 https://doi.org/10.1038/s41377-020-0251-x	journal	January 2020
Cell migration in microengineered tumor environments Um, Eujin; Oh, Jung Min; Granick, Steve Lab on a Chip, Vol. 17, Issue 24 https://doi.org/10.1039/c7lc00555e	journal	January 2017
A neural lens for super-resolution biological imaging Grant-Jacob, James A.; Mackay, Benita S.; Baker, James A. G. Journal of Physics Communications, Vol. 3, Issue 6 https://doi.org/10.1088/2399-6528/ab267d	journal	June 2019
Microplastic Identification via Holographic Imaging and Machine Learning Bianco, Vittorio; Memmolo, Pasquale; Carcagnì, Pierluigi Advanced Intelligent Systems, Vol. 2, Issue 2 https://doi.org/10.1002/aisy.201900153	journal	January 2020
Noninvasive detection of macrophage activation with single-cell resolution through machine learning Pavillon, Nicolas; Hobro, Alison J.; Akira, Shizuo Proceedings of the National Academy of Sciences, Vol. 115, Issue 12 https://doi.org/10.1073/pnas.1711872115	journal	March 2018
Machine learning analysis of extreme events in optical fibre modulation instability Närhi, Mikko; Salmela, Lauri; Toivonen, Juha Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-07355-y	journal	November 2018
Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation Tanhaemami, Mohammad; Alizadeh, Elaheh; Sanders, Claire K. https://doi.org/10.1101/497834	posted_content	June 2019
Cell dynamic morphology classification using deep convolutional neural networks: Cell Dynamic Morphology Li, Heng; Pang, Fengqian; Shi, Yonggang Cytometry Part A, Vol. 93, Issue 6 https://doi.org/10.1002/cyto.a.23490	journal	May 2018
Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines Lam, Van K.; Nguyen, Thanh; Phan, Thuc Cytometry Part A, Vol. 95, Issue 7 https://doi.org/10.1002/cyto.a.23774	journal	April 2019
Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm Purandare, Sanyogita; Zhu, Jinlong; Zhou, Renjie Optics Express, Vol. 27, Issue 13 https://doi.org/10.1364/oe.27.017743	journal	January 2019
Sensitivity and specificity evaluation of multiple neurodegenerative proteins for Creutzfeldt-Jakob disease diagnosis using a deep-learning approach Lee, Sol Moe; Hyeon, Jae Wook; Kim, Soo-Jin Prion, Vol. 13, Issue 1 https://doi.org/10.1080/19336896.2019.1639482	journal	January 2019
A novel machine learning based approach for iPS progenitor cell identification Zhang, Haishan; Shao, Ximing; Peng, Yin PLOS Computational Biology, Vol. 15, Issue 12 https://doi.org/10.1371/journal.pcbi.1007351	journal	December 2019
Time-stretch LiDAR as a spectrally scanned time-of-flight ranging camera Jiang, Yunshan; Karpf, Sebastian; Jalali, Bahram Nature Photonics, Vol. 14, Issue 1 https://doi.org/10.1038/s41566-019-0548-6	journal	December 2019
PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning Rivenson, Yair; Liu, Tairan; Wei, Zhensong arXiv https://doi.org/10.48550/arxiv.1807.07701	text	January 2018
Direct Observation of Spatiotemporal Dynamics of Short Electron Bunches in Storage Rings Evain, C.; Roussel, E.; Le Parquier, M. Physical Review Letters, Vol. 118, Issue 5 https://doi.org/10.1103/physrevlett.118.054801	journal	February 2017
Holographic deep learning for rapid optical screening of anthrax spores Jo, YoungJu; Park, Sangjin; Jung, JaeHwang Science Advances, Vol. 3, Issue 8 https://doi.org/10.1126/sciadv.1700606	journal	August 2017
Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry Li, Yueqin; Mahjoubfar, Ata; Chen, Claire Lifan Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-47193-6	journal	July 2019
Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging Wu, Yunzhao; Zhou, Yuqi; Huang, Chun-Jung Optics Express, Vol. 28, Issue 1 https://doi.org/10.1364/oe.380679	journal	January 2020
Automatic label‐free detection of breast cancer using nonlinear multimodal imaging and the convolutional neural network ResNet50 Ali, Nairveen; Quansah, Elsie; Köhler, Katarina Translational Biophotonics, Vol. 1, Issue 1-2 https://doi.org/10.1002/tbio.201900003	journal	December 2019
Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research Laurianne, David; Arús-Pous, Josep; Karlsson, Johan Zenodo https://doi.org/10.5281/zenodo.3529160	text	January 2019
Deep learning of diffraction image patterns for accurate classification of five cell types Jin, Jiahong; Lu, Jun Q.; Wen, Yuhua Journal of Biophotonics, Vol. 13, Issue 3 https://doi.org/10.1002/jbio.201900242	journal	December 2019
Learning from droplet flows in microfluidic channels using deep neural networks Pooria, Hadikhani,; Navid, Borhani,; H., Hashemi, S. Mohammad ETH Zurich https://doi.org/10.3929/ethz-b-000346527	text	January 2019
In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning Belashov, A. V.; Zhikhoreva, A. A.; Belyaeva, T. N. Journal of the Optical Society of America A, Vol. 37, Issue 2 https://doi.org/10.1364/JOSAA.382135	journal	January 2020
Learning from droplet flows in microfluidic channels using deep neural networks Hadikhani, Pooria; Borhani, Navid; H. Hashemi, S. Mohammad Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-44556-x	journal	May 2019
A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions Li, Xiaolin; Zhang, Yichi; Zhao, He arXiv https://doi.org/10.48550/arxiv.1805.02784	preprint	January 2018
Quantitative imaging of lipid droplets in single cells Gupta, Anushka; Dorlhiac, Gabriel F.; Streets, Aaron M. The Analyst, Vol. 144, Issue 3 https://doi.org/10.1039/c8an01525b	journal	January 2019
Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia Kobayashi, Hirofumi; Lei, Cheng; Wu, Yi Lab on a Chip, Vol. 19, Issue 16 https://doi.org/10.1039/c8lc01370e	journal	January 2019
Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research Laurianne, David; Arús-Pous, Josep; Karlsson, Johan Zenodo https://doi.org/10.5281/zenodo.3529159	text	January 2019
Label-free classification of cells based on supervised machine learning of subcellular structures Ozaki, Yusuke; Yamada, Hidenao; Kikuchi, Hirotoshi PLOS ONE, Vol. 14, Issue 1 https://doi.org/10.1371/journal.pone.0211347	journal	January 2019
Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation Tanhaemami, Mohammad; Alizadeh, Elaheh; Sanders, Claire K. Physical Biology, Vol. 16, Issue 5 https://doi.org/10.1088/1478-3975/ab2c60	journal	July 2019
Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach Yao, Ruoyang; Ochoa, Marien; Yan, Pingkun Light: Science & Applications, Vol. 8, Issue 1 https://doi.org/10.1038/s41377-019-0138-x	journal	March 2019
Matrix Analysis of Warped Stretch Imaging Kim, Chanju; Mahjoubfar, Ata; Chan, Jacky C. K. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-11238-5	journal	September 2017
Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning Kobayashi, Hirofumi; Lei, Cheng; Wu, Yi Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-12378-4	journal	September 2017
A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction Fan, Ke; Zhang, Sheng; Zhang, Ying Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-13680-x	journal	October 2017
Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis Radhakrishnan, Adityanarayanan; Damodaran, Karthik; Soylemezoglu, Ali C. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-17858-1	journal	December 2017
All-passive pixel super-resolution of time-stretch imaging Chan, Antony C. S.; Ng, Ho-Cheung; Bogaraju, Sharat C. V. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/srep44608	journal	March 2017
Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features Pang, Fengqian; Li, Heng; Shi, Yonggang Journal of Computational Biology, Vol. 25, Issue 8 https://doi.org/10.1089/cmb.2018.0023	journal	August 2018
Assessing microscope image focus quality with deep learning Yang, Samuel J.; Berndl, Marc; Michael Ando, D. BMC Bioinformatics, Vol. 19, Issue 1 https://doi.org/10.1186/s12859-018-2087-4	journal	March 2018
Context-Aware Image Compression Chan, Jacky C. K.; Mahjoubfar, Ata; Chen, Claire L. PLOS ONE, Vol. 11, Issue 7 https://doi.org/10.1371/journal.pone.0158201	journal	July 2016
The Temporal Gearbox Jalali, Bahram; Chan, Jacky C. K.; Mahjoubfar, Ata arXiv https://doi.org/10.48550/arxiv.1612.04562	preprint	January 2016
Time Stretch Inspired Computational Imaging Jalali, Bahram; Suthar, Madhuri; Asghari, Mohamad arXiv https://doi.org/10.48550/arxiv.1706.07841	preprint	January 2017
ParasNet: Fast Parasites Detection with Neural Networks Xu, X. F.; Talbot, S.; Selvaraja, T. arXiv https://doi.org/10.48550/arxiv.2002.11327	preprint	January 2020

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