matlab bio-formats package Search Results


omero  (Glencoe Software Inc)
95
Glencoe Software Inc omero
Omero, supplied by Glencoe Software Inc, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/omero/product/Glencoe Software Inc
Average 95 stars, based on 1 article reviews
omero - by Bioz Stars, 2026-05
95/100 stars
  Buy from Supplier
zeiss .czi files  (Carl Zeiss)
90
Carl Zeiss zeiss .czi files
Zeiss .Czi Files, supplied by Carl Zeiss, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/zeiss .czi files/product/Carl Zeiss
Average 90 stars, based on 1 article reviews
zeiss .czi files - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier
r2020a software  (MathWorks Inc)
96
MathWorks Inc r2020a software
R2020a Software, 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/result/r2020a software/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
r2020a software - by Bioz Stars, 2026-05
96/100 stars
  Buy from Supplier
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/result/deep learning toolbox standalone application/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
deep learning toolbox standalone application - by Bioz Stars, 2026-05
96/100 stars
  Buy from Supplier
zen core microscopy software  (Carl Zeiss)
90
Carl Zeiss zen core microscopy software
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.
Zen Core Microscopy Software, supplied by Carl Zeiss, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/zen core microscopy software/product/Carl Zeiss
Average 90 stars, based on 1 article reviews
zen core microscopy software - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

Image Search Results


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