Finding a Suitable Class Distribution for Building Histological Images Datasets Used in Deep Model Training-The Case of Cancer Detection.

The class distribution of a training dataset 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.

Evaluation of eight melanocytic and neural crest-associated markers in a well-characterised series of 124 malignant peripheral nerve sheath tumours (MPNST): useful to distinguish MPNST from melanoma?

Aims: The diagnosis of malignant peripheral nerve sheath tumour (MPNST) may be challenging, especially in the sporadic setting. Owing to the lack of specific histological criteria, immunohistochemical and molecular diagnostic markers, several differential diagnoses must be considered, especially melanoma. Indeed, although S100 protein usually stains melanoma, other melanocytic markers are often negative, especially in spindle cell/desmoplastic types.