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deep learning toolbox standalone application  (MathWorks Inc)


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    MathWorks Inc deep learning toolbox standalone application
    Fig. 2 | The overall workflow of the Deepometry procedure. Step 1 (not shown here) guides users through the installation of the software and packages required to run Deepometry (Python/MATLAB, option A or B) or installation of the <t>standalone</t> application (MATLAB, option C). The application of Deepometry to image data analysis starts with Step 2. Steps 2 and 3, 6–8 and 12 and 13 are preprocessing actions for the training set, validation set and testing set, respectively, served to transform raw input images to data types and shapes appropriate for deep learning operations. Steps 4 and 5 are model training actions (highlighted in red). Steps 9–11 and 14–16 are predicting mechanisms for annotated data (highlighted in cyan) and unannotated data (highlighted in purple), respectively. Steps 17–21 are used to extract deep learning feature embeddings for dimension reduction and data exploration.
    Deep Learning Toolbox Standalone Application, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 923 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/deep+learning+toolbox+standalone+application/Deep+Learning+Toolbox/10__1038_slash_s41596___021___00549___7-210-74-53
    Average 96 stars, based on 923 article reviews
    deep learning toolbox standalone application - by Bioz Stars, 2026-09
    96/100 stars

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    1) Product Images from "Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry"

    Article Title: Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

    Journal: Nature Protocols

    doi: 10.1038/s41596-021-00549-7

    Fig. 2 | The overall workflow of the Deepometry procedure. Step 1 (not shown here) guides users through the installation of the software and packages required to run Deepometry (Python/MATLAB, option A or B) or installation of the standalone application (MATLAB, option C). The application of Deepometry to image data analysis starts with Step 2. Steps 2 and 3, 6–8 and 12 and 13 are preprocessing actions for the training set, validation set and testing set, respectively, served to transform raw input images to data types and shapes appropriate for deep learning operations. Steps 4 and 5 are model training actions (highlighted in red). Steps 9–11 and 14–16 are predicting mechanisms for annotated data (highlighted in cyan) and unannotated data (highlighted in purple), respectively. Steps 17–21 are used to extract deep learning feature embeddings for dimension reduction and data exploration.
    Figure Legend Snippet: Fig. 2 | The overall workflow of the Deepometry procedure. Step 1 (not shown here) guides users through the installation of the software and packages required to run Deepometry (Python/MATLAB, option A or B) or installation of the standalone application (MATLAB, option C). The application of Deepometry to image data analysis starts with Step 2. Steps 2 and 3, 6–8 and 12 and 13 are preprocessing actions for the training set, validation set and testing set, respectively, served to transform raw input images to data types and shapes appropriate for deep learning operations. Steps 4 and 5 are model training actions (highlighted in red). Steps 9–11 and 14–16 are predicting mechanisms for annotated data (highlighted in cyan) and unannotated data (highlighted in purple), respectively. Steps 17–21 are used to extract deep learning feature embeddings for dimension reduction and data exploration.

    Techniques Used: Software, Biomarker Discovery

    Related Articles

    Software:

    Article Title: Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry
    Article Snippet: For those wishing to customize or explore individual modules, the following packages are used.For those wishing to customize or explore individual modules, the following packages are used.. Essential packages for the Python environment (see more details and download sites in the installation guide in Supplementary Note 1): ● Python 3.6 ● Tensorflow-gpu 1.9.0 ● Keras 2.1.5 ● Numpy 1.18.1 ● Scipy 1.4.1 ● Keras-resnet 0.0.7 ● Java Development Kit 8.0/11.0 ● Python-bioformats 1.5.2 ● Jupyter notebook Essential packages for the MATLAB environment (see more details and download sites in the installation guide in Supplementary Note 2): ● Image processing toolbox ● Deep Learning Toolbox Standalone application (the details and download sites are given in the installation guide in Supplementary Note 3)

    Biomarker Discovery:

    Article Title: Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry
    Article Snippet: For those wishing to customize or explore individual modules, the following packages are used.For those wishing to customize or explore individual modules, the following packages are used.. Essential packages for the Python environment (see more details and download sites in the installation guide in Supplementary Note 1): ● Python 3.6 ● Tensorflow-gpu 1.9.0 ● Keras 2.1.5 ● Numpy 1.18.1 ● Scipy 1.4.1 ● Keras-resnet 0.0.7 ● Java Development Kit 8.0/11.0 ● Python-bioformats 1.5.2 ● Jupyter notebook Essential packages for the MATLAB environment (see more details and download sites in the installation guide in Supplementary Note 2): ● Image processing toolbox ● Deep Learning Toolbox Standalone application (the details and download sites are given in the installation guide in Supplementary Note 3)



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    deep learning toolbox standalone application  (MathWorks Inc)
    96
    MathWorks Inc deep learning toolbox standalone application
    Fig. 2 | The overall workflow of the Deepometry procedure. Step 1 (not shown here) guides users through the installation of the software and packages required to run Deepometry (Python/MATLAB, option A or B) or installation of the <t>standalone</t> application (MATLAB, option C). The application of Deepometry to image data analysis starts with Step 2. Steps 2 and 3, 6–8 and 12 and 13 are preprocessing actions for the training set, validation set and testing set, respectively, served to transform raw input images to data types and shapes appropriate for deep learning operations. Steps 4 and 5 are model training actions (highlighted in red). Steps 9–11 and 14–16 are predicting mechanisms for annotated data (highlighted in cyan) and unannotated data (highlighted in purple), respectively. Steps 17–21 are used to extract deep learning feature embeddings for dimension reduction and data exploration.
    Deep Learning Toolbox Standalone Application, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/deep+learning+toolbox+standalone+application/Deep+Learning+Toolbox/10__1038_slash_s41596___021___00549___7-210-74-53
    Average 96 stars, based on 1 article reviews
    deep learning toolbox standalone application - by Bioz Stars, 2026-09
    96/100 stars
    		  Buy from Supplier

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    Fig. 2 | The overall workflow of the Deepometry procedure. Step 1 (not shown here) guides users through the installation of the software and packages required to run Deepometry (Python/MATLAB, option A or B) or installation of the standalone application (MATLAB, option C). The application of Deepometry to image data analysis starts with Step 2. Steps 2 and 3, 6–8 and 12 and 13 are preprocessing actions for the training set, validation set and testing set, respectively, served to transform raw input images to data types and shapes appropriate for deep learning operations. Steps 4 and 5 are model training actions (highlighted in red). Steps 9–11 and 14–16 are predicting mechanisms for annotated data (highlighted in cyan) and unannotated data (highlighted in purple), respectively. Steps 17–21 are used to extract deep learning feature embeddings for dimension reduction and data exploration.

    Journal: Nature Protocols

    Article Title: Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

    doi: 10.1038/s41596-021-00549-7

    Figure Lengend Snippet: Fig. 2 | The overall workflow of the Deepometry procedure. Step 1 (not shown here) guides users through the installation of the software and packages required to run Deepometry (Python/MATLAB, option A or B) or installation of the standalone application (MATLAB, option C). The application of Deepometry to image data analysis starts with Step 2. Steps 2 and 3, 6–8 and 12 and 13 are preprocessing actions for the training set, validation set and testing set, respectively, served to transform raw input images to data types and shapes appropriate for deep learning operations. Steps 4 and 5 are model training actions (highlighted in red). Steps 9–11 and 14–16 are predicting mechanisms for annotated data (highlighted in cyan) and unannotated data (highlighted in purple), respectively. Steps 17–21 are used to extract deep learning feature embeddings for dimension reduction and data exploration.

    Article Snippet: Essential packages for the Python environment (see more details and download sites in the installation guide in Supplementary Note 1): ● Python 3.6 ● Tensorflow-gpu 1.9.0 ● Keras 2.1.5 ● Numpy 1.18.1 ● Scipy 1.4.1 ● Keras-resnet 0.0.7 ● Java Development Kit 8.0/11.0 ● Python-bioformats 1.5.2 ● Jupyter notebook Essential packages for the MATLAB environment (see more details and download sites in the installation guide in Supplementary Note 2): ● Image processing toolbox ● Deep Learning Toolbox Standalone application (the details and download sites are given in the installation guide in Supplementary Note 3)

    Techniques: Software, Biomarker Discovery