A train-free method that compresses 3D medical volumes into small embeddings via a frozen 2D foundation model and random projections, outperforming several medical-volume pretrained models on benchmark tasks.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
A train-free method that compresses 3D medical volumes into small embeddings via a frozen 2D foundation model and random projections, outperforming several medical-volume pretrained models on benchmark tasks.