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DAB stain_​AZ_​TN

This workflow was built with KNIME Analytics Platform 4.0.2, however, similar workflows have been used with KNIME Analytics Platform 3.5 to 4.1 as well.

The sample images and pixel classification model are packaged with this workflow already, but can also be downloaded from https://doi.org/10.6084/m9.figshare.9936287.v2.

Instructions to follow before opening the workflow:
1) Follow instructions at https://knime.com/community/imagej to install the KNIME Image Processing - ImageJ Extension
2) Follow instructions at https://knime.com/wiki/knime-image-processing-nightly-build to add and activate the nightly software site of KNIME Image Processing
3) Go to File > Preferences > KNIME > Image Processing Plugin and add ",F" to the available dimension labels and restart KNIME Analytics Platform
4) Open the workflow and follow instructions to search and install missing extensions

This workflow was built with KNIME Analytics Platform 4.0.2, however, similar workflows have beenused with KNIME Analytics Platform 3.5 to 4.1 as well.The sample images and pixel classification model are packaged with this workflow already, but canalso be downloaded from https://doi.org/10.6084/m9.figshare.9936287.v2.Instructions to follow before opening the workflow:1) Follow instructions at https://knime.com/community/imagej to install the KNIME Image Processing -ImageJ Extension2) Follow instructions at https://knime.com/wiki/knime-image-processing-nightly-build to add andactivate the nightly software site of KNIME Image Processing3) Go to File > Preferences > KNIME > Image Processing Plugin and add ",F" to the availabledimension labels and restart KNIME Analytics Platform4) Open the workflow and follow instructions to search and install missing extensions If a model is available directly, use the Model Readernode instead of re-training a model. View resulted segmented image applyed to input ImageSelect folder withinput imagesOpen imagesWrite model to filefor re-use in differentworkflowRead a saved Model Split into trainingand test imagesNode 31Node 32Insert node between Row Splitter andTrain/Apply Pixel Classification Modelif you run out of memoryPre-Classification ImageJ2 Post-Classification Labeling InteractiveSegmentation View List Files Image Reader(Table) Model Writer Model Reader Row Splitter Train PixelClassification Model Apply PixelClassification Model Row Sampling This workflow was built with KNIME Analytics Platform 4.0.2, however, similar workflows have beenused with KNIME Analytics Platform 3.5 to 4.1 as well.The sample images and pixel classification model are packaged with this workflow already, but canalso be downloaded from https://doi.org/10.6084/m9.figshare.9936287.v2.Instructions to follow before opening the workflow:1) Follow instructions at https://knime.com/community/imagej to install the KNIME Image Processing -ImageJ Extension2) Follow instructions at https://knime.com/wiki/knime-image-processing-nightly-build to add andactivate the nightly software site of KNIME Image Processing3) Go to File > Preferences > KNIME > Image Processing Plugin and add ",F" to the availabledimension labels and restart KNIME Analytics Platform4) Open the workflow and follow instructions to search and install missing extensions If a model is available directly, use the Model Readernode instead of re-training a model. View resulted segmented image applyed to input ImageSelect folder withinput imagesOpen imagesWrite model to filefor re-use in differentworkflowRead a saved Model Split into trainingand test imagesNode 31Node 32Insert node between Row Splitter andTrain/Apply Pixel Classification Modelif you run out of memoryPre-Classification ImageJ2 Post-Classification Labeling InteractiveSegmentation View List Files Image Reader(Table) Model Writer Model Reader Row Splitter Train PixelClassification Model Apply PixelClassification Model Row Sampling

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