Implementing machine learning methods for imaging flow cytometry.

In this review, we focus on the applications of machine learning methods for analyzing image data acquired in imaging flow cytometry technologies. We propose that the analysis approaches can be categorized into two groups based on the type of data, raw imaging signals or features explicitly extracted from images, being analyzed by a trained model. We hope that this categorization is helpful for understanding uniqueness, differences and opportunities when the machine learning-based analysis is implemented in recently developed 'imaging' cell sorters.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Li Pei,et al.  Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry , 2019, Scientific Reports.

[3]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[4]  Inderjit S. Dhillon,et al.  Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.

[5]  Anne E Carpenter,et al.  Reconstructing cell cycle and disease progression using deep learning , 2017, Nature Communications.

[6]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[7]  Leopold Parts,et al.  Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning , 2016, G3: Genes, Genomes, Genetics.

[8]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[9]  Mary M. Maleckar,et al.  Generative Modeling with Conditional Autoencoders: Building an Integrated Cell , 2017, 1705.00092.

[10]  Olaf Wolkenhauer,et al.  Diagnostic Potential of Imaging Flow Cytometry. , 2018, Trends in biotechnology.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Bahram Jalali,et al.  High-throughput single-microparticle imaging flow analyzer , 2012, Proceedings of the National Academy of Sciences.

[13]  Ryoichi Horisaki,et al.  Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics , 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[14]  Euan A. Ashley,et al.  Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments , 2016, PLoS Comput. Biol..

[15]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Fumihito Arai,et al.  Intelligent Image-Activated Cell Sorting , 2018, Cell.

[18]  Hai Su,et al.  Deep Learning in Microscopy Image Analysis: A Survey , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[20]  Brendan J. Frey,et al.  Classifying and segmenting microscopy images with deep multiple instance learning , 2015, Bioinform..

[21]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[22]  G Valet,et al.  Fast imaging in flow: a means of combining flow-cytometry and image analysis. , 1979, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[23]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

[24]  Anca Ciurte,et al.  Automatic detection of circulating tumor cells in darkfield microscopic images of unstained blood using boosting techniques , 2018, PloS one.

[25]  William E. Ortyn,et al.  Cellular image analysis and imaging by flow cytometry. , 2007, Clinics in laboratory medicine.

[26]  Kenji Yasuda,et al.  An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution , 2017, Scientific Reports.

[27]  Kenji Yasuda,et al.  Non-destructive on-chip imaging flow cell-sorting system for on-chip cellomics , 2013 .

[28]  William Graf,et al.  Deep learning for cellular image analysis , 2019, Nature Methods.

[29]  Facundo D. Batista,et al.  Asymmetric Segregation of Polarized Antigen on B Cell Division Shapes Presentation Capacity , 2012, Science.

[30]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[31]  Fabian J Theis,et al.  Prospective identification of hematopoietic lineage choice by deep learning , 2017, Nature Methods.

[32]  Andrew J. deMello,et al.  High-Throughput Multi-parametric Imaging Flow Cytometry , 2017 .

[33]  V. Vapnik.  Pattern recognition using generalized portrait method , 1963 .

[34]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[35]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  L L Wheeless,et al.  Imaging in flow. , 1979, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[37]  David Duvenaud,et al.  Invertible Residual Networks , 2018, ICML.

[38]  Takanori Ichiki,et al.  Non-destructive on-chip cell sorting system with real-time microscopic image processing , 2004, Journal of nanobiotechnology.

[39]  Kevin M. Cury,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

[40]  Yolanda T. Chong,et al.  Automated analysis of high‐content microscopy data with deep learning , 2017, Molecular systems biology.

[41]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[42]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[43]  Jie Li,et al.  Machine Learning Based Real‐Time Image‐Guided Cell Sorting and Classification , 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[44]  Prafulla Dhariwal,et al.  Glow: Generative Flow with Invertible 1x1 Convolutions , 2018, NeurIPS.

[45]  Oleksandr Bailo,et al.  Red Blood Cell Image Generation for Data Augmentation Using Conditional Generative Adversarial Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[46]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[47]  Anne E Carpenter,et al.  Label-free cell cycle analysis for high-throughput imaging flow cytometry , 2016, Nature Communications.

[48]  Anne E Carpenter,et al.  Leveraging machine vision in cell-based diagnostics to do more with less , 2019, Nature Materials.

[49]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[50]  Natasha S. Barteneva,et al.  Imaging flow cytometry analysis of intracellular pathogens. , 2017, Methods.

[51]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[52]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[53]  Namrata Anand,et al.  Generative modeling for protein structures , 2018, NeurIPS.