Free and open source

Description

Set of KNIME workflows for the training of a deep learning model for image-classification with custom images and classes.

The workflows take ground-truth category annotations as a table generated by the qualitative annotations plugins in Fiji.

Workflows for the training of a model AND for the prediction of image-category for new images are provided.

There are different workflows if you do:

- binary image-classification (images get classified in 1 category out of 2 possible categories) 

- classification from possibly more than 2 categories (images are classified in 1 category out of N possible categories).

The training workflows take care of image pre-processing and allows the visualization of the training and validation losses in real time along the training.  

For the training, transfer learning from a pre-trained VGG16 base is performed, with freshly initialized fully connected layers.

Only the fully connected layers are trained, the VGG16 base is frozen is this workflow, but once the fully connected layers trained the base could also be finetuned. In practice, it often works well with the frozen base.

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Description

Set of Fiji plugins facilitating the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets.

The plugins can be installed by activating the Qualitative annotations plugins in Fiji.

GUI
Description

Analyze the clustering behavior of nuclei in 3D images. The centers of the nuclei are detected. The nuclei are filtered by the presence of a signal in a different channel. The clustering is done with the density based algorithm DBSCAN. The nearest neighbor distances between all nuclei and those outside and inside of the clusters are calculated.

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Description

KNIME workflow to visualize a dataset described by multiple quantitative features (ex: a list of samples or cells, each described with multiple morphological features) as a 3D cloud of points (each point corresponding to one sample/cell) as well as a line plot (1 line per sample/cell).

For the 3D plot, the workflow uses Principal Component Analysis (PCA) for dimensionality reduction, ie it simplifies the information for each sample from n-features to 3 pseudo-features which are used as x,y,z-coordinates for each sample. The original features should cover similar value range, to make sure the PCA is not biased towards the large values features. One option is to normalize the values (min/max or Z-score). 

Also make sure that the resulting PCA represents a decent % of the original data variance (at least 70%). Otherwise the PCA plot will not be representative of the original data-distribution. The % is shown in the title of the PCA plot.

The workflow is interactive and so selecting in one panel of the figure will highlight in the other panel too.

It was originally published for the visualization of phenotypic kidney features in zebrafish, but the workflow is generic by design and can be reused for any quantitative feature set. 

KNIME-Workflow
Description

The library contains several helper functions to generate MoBIE project folders and add data to it.  Itis a python library to generate data in the MoBIE data storage layout. 

For further information, look to http://biii.eu/mobie-fiji-viewer

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