MPNST (in NF1 and a sporadic setting) can quite often be positive for Melan-A, tyrosinase and MITF. Pathologists should be cognisant of these exceptions to prevent confusion with melanoma.

The class distribution of a training data set is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and clas-

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