Using real multi-view radiographs from the same study as self-supervised training pairs yields better anatomical representations and downstream veterinary task performance than synthetic single-image augmentations.
Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging
Using real multi-view radiographs from the same study as self-supervised training pairs yields better anatomical representations and downstream veterinary task performance than synthetic single-image augmentations.