ArticleFrontiers in veterinary science2021
Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study.
Article in Frontiers in veterinary science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- Generative AI in Veterinary Pathology: Feasibility of a GPT-Based Assistive Tool for Gross, Cytologic, and Histopathologic Assessment of Canine Cutaneous Neoplasms-A Pilot Study.Animals : an open access journal from MDPI · 2026Article
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
- Histopathological diagnosis of Ovine Pulmonary Adenocarcinoma (OPA) based on ensemble model.BMC veterinary research · 2026Article
- The application of artificial intelligence in veterinary oncology: a scoping review.BMC veterinary research · 2025Article
- Tumor immune microenvironment and immune phenotypes in PD-L1-tested canine urothelial carcinoma.BMC veterinary research · 2025Article
- Cutaneous plasmacytoma with Mott cell differentiation in a dog.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2024Article
- Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.Veterinary pathology · 2023Article
- Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis.Animals : an open access journal from MDPI · 2023Article
- Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning.Frontiers in veterinary science · 2023Article
- Review
- Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.Scientific data · 2022Article
- Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Canine cutaneous round cell tumors (RCT) represent one of the routine diagnostic challenges for veterinary pathologists. Computer-aided approaches are developed to overcome these restrictions and to increase accuracy and consistency of diagnosis. These systems are also of high benefit reducing errors when a large number of cases are screened daily. In this study we describe ARCTA (Automated Round Cell Tumors Assessment), a fully automated algorithm for cutaneous RCT classification and mast cell tumors grading in canine histopathological images. ARCTA employs a deep learning strategy and was developed on 416 RCT images and 213 mast cell tumors images. In the test set, our algorithm exhibited an excellent classification performance in both RCT classification (accuracy: 91.66%) and mast cell tumors grading (accuracy: 100%). Misdiagnoses were encountered for histiocytomas in the train set and for melanomas in the test set. For mast cell tumors the reduction of a grade was observed in the train set, but not in the test set. To the best of our knowledge, the proposed model is the first fully automated algorithm in histological images specifically developed for veterinary medicine. Being very fast (average computational time 2.63 s), this algorithm paves the way for an automated and effective evaluation of canine tumors.
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