Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.

ABSTRACT: 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 segmentatio

SUBMITTER: Wilm F 

PROVIDER: S-EPMC9515104 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature