  The NuCLS dataset contains over 220.000 annotations of cell nuclei in breast
cancers. We show how to use these data to create a multi-rater model with the
MIScnn Framework to automate the analysis of cell nuclei. For the model
creation, we use the widespread U-Net approach embedded in a pipeline. This
pipeline provides besides the high performance convolution neural network,
several preprocessor techniques and a extended data exploration. The final
model is tested in the evaluation phase using a wide variety of metrics with a
subsequent visualization. Finally, the results are compared and interpreted
with the results of the NuCLS study. As an outlook, indications are given which
are important for the future development of models in the context of cell
nuclei.
