Magnetic resonance imaging–based radiation treatment plans for dogs may be feasible with the use of generative adversarial networks
Implementing generative learning into veterinary radiation therapy planning demonstrates the potential to reduce imaging costs and time.
Authors
- Amin Komeili (ORCID: https://orcid.org/0000-0002-0938-6099)
- Valérie J. Poirier (ORCID: https://orcid.org/0000-0002-0170-3934)
- Eranga Ukwatta (ORCID: https://orcid.org/0000-0003-0180-4716)
- Christopher J. Pinard (ORCID: https://orcid.org/0000-0003-1311-5467)
- Ryan Appleby (ORCID: https://orcid.org/0000-0002-5515-8628)
- Nicola Billings
Institutions
- Sunnybrook Health Science Centre (CA)
- University of Calgary (CA)
- Lakeshore General Hospital (CA)
- Health Sciences Centre (CA)
- University of Guelph (CA)
Publication Details
- Journal
- American Journal of Veterinary Research
- Published
- 2025-03-17
- DOI
- https://doi.org/10.2460/ajvr.24.08.0248
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00