Publikationsdatenbank

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset (2022)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Wilm, Frauke

Fragoso, Marco (WE 12)

Marzahl, Christian

Qiu, Jingna

Puget, Chloé (WE 12)

Diehl, Laura (WE 12)

Bertram, Christof A.

Klopfleisch, Robert (WE 12)

Maier, Andreas

Breininger, Katharina

Aubreville, Marc

Quelle

Scientific data

Bandzählung: 9

Seiten: Artikel 588

ISSN: 2052-4463

Sprache

Englisch

Verweise

URL (Volltext): https://www.nature.com/articles/s41597-022-01692-w

DOI: 10.1038/s41597-022-01692-w

Pubmed: 36167846

Kontakt

Institut für Tierpathologie

Robert-von-Ostertag-Str. 15
14163 Berlin
+49 30 838 62450
pathologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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.