Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models
The ViT and gMLP exhibited comparable performance with the CNN (the current state-of-the-art). However, for certain tasks, there was a significant difference in the performance of the ViT and gMLP when compared to the CNN. This difference in model performance for various tasks proves that the capabilities of different architectures may be leveraged.
Authors
- Mouzhi Ge (ORCID: https://orcid.org/0000-0002-4107-5303)
- Georgi Chaltikyan (ORCID: https://orcid.org/0000-0003-0743-2036)
- Martin Gerdes (ORCID: https://orcid.org/0000-0003-4408-5838)
- Christian W Omlin
- Fara Aninha Fernandes (ORCID: https://orcid.org/0000-0002-8522-5166)
Institutions
- University of Agder (NO)
- Deggendorf Institute of Technology (DE)
Publication Details
- Journal
- Dentomaxillofacial Radiology
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1093/dmfr/twae056
- Citations
- 4
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.97
Funders
- Universitetet i Agder