  Due to morphological similarities, the differentiation of histologic sections
of cutaneous tumors into individual subtypes can be challenging. Recently, deep
learning-based approaches have proven their potential for supporting
pathologists in this regard. However, many of these supervised algorithms
require a large amount of annotated data for robust development. We present a
publicly available dataset of 350 whole slide images of seven different canine
cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic
classes, including seven cutaneous tumor subtypes. In inter-rater experiments,
we show a high consistency of the provided labels, especially for tumor
annotations. We further validate the dataset by training a deep neural network
for the task of tissue segmentation and tumor subtype classification. We
achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in
particular. For classification, we achieve a slide-level accuracy of 0.9857.
Since canine cutaneous tumors possess various histologic homologies to human
tumors the added value of this dataset is not limited to veterinary pathology
but extends to more general fields of application.
