ArticleScientific data2022
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial intelligence predictsVeterinary pathology · 2026Article
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- The application of artificial intelligence in veterinary oncology: a scoping review.BMC veterinary research · 2025Article
- Knowledge, Perceptions, Attitudes, and Practices of Dog and Cat Owners Regarding Skin Tumors: A Cross-Sectional Study.Veterinary sciences · 2025Article
- Fully automatic HER2 tissue segmentation for interpretable HER2 scoring.Journal of pathology informatics · 2025Article
- Article
- Attention induction based on pathologist annotations for improving whole slide pathology image classifier.Journal of pathology informatics · 2025Article
- Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classification.PloS one · 2025Article
- Review of Molecular Technologies for Investigating Canine Cancer.Animals : an open access journal from MDPI · 2024Review
- Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.Scientific reports · 2023Article
- Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.Veterinary pathology · 2023Article
- Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.Scientific data · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.