A multi-scale convolutional neural network for phenotyping high-content cellular images

Godinez, William J.; Hossain, Imtiaz; Lazic, Stanley E.; Davies, John W.; Zhang, Xian

doi:10.1093/bioinformatics/btx069

Cited by 150 publications

(127 citation statements)

References 47 publications

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“…The MoA annotation comes both from visual inspection by experts as well as scientific literature, using a course-grained set of 12 MoAs, with each MoA containing multiple compounds and concentrations. This subset with ground truth annotation is widely used to benchmark diverse analysis methods [Ljosa et al, 2013, Kandaswamy et al, 2016, Godinez et al, 2017, Ando et al, 2017. We developed the Bayesian approach as described above, with the same Multi-scale Convolutional architecture neural network (MCNN) architecture we previous designed [Godinez et al, 2017].…”

Section: Cellular Microscopy: Bbbc021mentioning

confidence: 99%

“…This subset with ground truth annotation is widely used to benchmark diverse analysis methods [Ljosa et al, 2013, Kandaswamy et al, 2016, Godinez et al, 2017, Ando et al, 2017. We developed the Bayesian approach as described above, with the same Multi-scale Convolutional architecture neural network (MCNN) architecture we previous designed [Godinez et al, 2017]. For validation, we adopted the rigorous leave-one-compound-out process, where in each session all except one compound was used for training and the hold-out compound was used for validation.…”

Section: Cellular Microscopy: Bbbc021mentioning

confidence: 99%

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Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

Deodato

Ball

Zhang

2019

Preprint

Self Cite

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Over the last decades, deep learning models have rapidly gained popularity for their ability to achieve state-of-the-art performances in different inference settings. Deep neural networks have been applied to an increasing number of problems spanning different domains of application. Novel applications define a new set of requirements that transcend accurate predictions and depend on uncertainty measures. The aims of this study are to implement Bayesian neural networks and use the corresponding uncertainty estimates to perform predictions and dataset analysis. We identify two main advantages in modeling the predictive uncertainty of deep neural networks performing classification tasks. The first is the possibility to discard highly uncertain predictions to be able to guarantee a higher accuracy of the remaining predictions. The second is the identification of unfamiliar patterns in the data that correspond to outliers in the model representation of the training data distribution. Such outliers can be further characterized as either corrupted observations or data belonging to different domains. Both advantages are well demonstrated with the benchmark datasets. Furthermore we apply the Bayesian approach to a biomedical imaging dataset where cancer cells are treated with diverse drugs, and show how one can increase classification accuracy and identify noise in the ground truth labels with uncertainty analysis.

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Section: Cellular Microscopy: Bbbc021mentioning

confidence: 99%

Section: Cellular Microscopy: Bbbc021mentioning

confidence: 99%

Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

Deodato

Ball

Zhang

2019

Preprint

Self Cite

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“…While these tools allow us to acquire substantially more data, it is still incumbent on the scientist to transform data into useful and actionable information. Manual image processing is unfeasible for those datasets, and automated data analysis techniques and methods are continuously being developed, even more now with the advent of machine learning tools, that often perform on par with human observers. Open‐source artificial intelligence (AI) software libraries such as Keras and TensorFlow, enable the power of neural networks and AI for the analysis of the datasets.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

González

Evans

2019

BioEssays

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Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions.

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“…Phenotyping cells with more subtle morphologies has been largely restricted to binary classification in order to detect specific alterations resulting from disease 10,[14][15][16][17] . Another approach is to use brightfield microscopy images to predict the location of immune-stains on sub-cellular structures in order to identify organelles [18][19][20][21] . However, a further step is required to interpret these stains to establish the cell phenotype.…”

Section: Introductionmentioning

confidence: 99%

Image-based Cell Phenotyping Using Deep Learning

Berryman

Matthews

Lee

et al. 2019

Preprint

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The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is unclear if this approach can reliably phenotype cells indistinguishable to the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average classification accuracy of 94.6%, tested using separately acquired images. Our results demonstrate the potential to develop an "electronic eye" to phenotype cells directly from microscopy images indistinguishable to the human eye.Image Based Cell Phenotyping Using Deep-Learning

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A multi-scale convolutional neural network for phenotyping high-content cellular images

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 150 publications

References 47 publications

Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

Image-based Cell Phenotyping Using Deep Learning

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