Deep Learning Module


The Deep Learning Extension enables to learn numerical feature descriptors of cell-objects directly from image pixels using deep convolutional autoencoders.


The bundled installer contains a demo data set. Starting the Deep Learning Module will automatically load this data set for convenience. If you want to use it on your own data, please, see the list of prerequisites below:

The graphical user interface

The graphical user interface is quite simple.

How to choose good training parameters?

The optimization strategy

Training an autoencoder is a rather complex multi-parametric optimization problem. The optimization algorithm works iteratively. Each training round is called an epoch. In each epoch, the current status of the autoencoder is evaluated and the algorithm proposes how to change each internal parameter of the autoencoder to achieve a better performance in the next round. The intensity of this proposed change of each internal parameters can be chosen. However, it is not guarenteed, that the autoencoder acutally improves after an epoch on held out data.

The tuning parameters

Visualizing results

After cell-object features have been learned with the Deep Learning Module, start the CellCognition Explorer main graphical user interface and open the generated file (.hdf). It should look similar to this: