Published March 17, 2026
| Version v2
Dataset
Open
VetXRay - A Dataset of 9,882 Manually Annotated Canine and Feline Thoracic Radiographs with Lesion and Image Quality Annotations
Authors/Creators
-
1.
University of Padua
- 2. Veterinary Teaching Hospital, University of Padua, Italy: Padua, IT
-
3.
Sano – Centre for Computational Personalised Medicine International Research Foundation
- 4. ShenAI
-
5.
AGH University of Krakow
Description
This dataset contains 9882 individual canine and feline radiographs annotated by experienced radiologist for both lesion and image quality. 17 different pathological findings have been annotated together with five different quality related tags. The .xlsx file contains the list of the files along with both the quality and lesion tags. A notebook showing how to load a single chest X-ray from the VetXRay dataset, retrieve its annotation from the metadata spreadsheet, apply basic image preprocessing, and display the result is also included.
Files
Image upload.ipynb
Files
(40.8 GB)
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md5:45c46fd5af3e562dadeeafc198c9a628
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Additional details
References
- Banzato T(corresponding author), Wodzinski M, Burti S, Osti VL, Rossoni V, Atzori M, Zotti A. Automatic classification of canine thoracic radiographs using deep learning. Sc.Rep. 2021 Feb 17;11(1):3964. doi: 10.1038/s41598-021-83515-3. PMID: 33597566; PMCID: PMC7889925.
- Burti S., Longhin Osti V, Zotti A, Banzato T. Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs. The Veterinary Journal 2020. doi: 10.1016/j.tvjl.2020.10550
- Banzato T. (corresponding author), Wodzinski M., Burti S., Vettore E., Muller H., Zotti A. An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs. (2023) Scientific Reports, 13 (1), DOI: 10.1038/s41598-023-44089-4.
- Banzato T(Corresponding author), Wodzinski M, Tauceri F, Donà C, Scavazza F, Müller H, Zotti A. An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats (2021) Frontiers in Veterinary Science, 8, 172 . DOI: 10.3389/fvets.2021.731936