RAPTOR: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
ICML 2025 Spotlight, 2025
*Equal contribution; Spotlight (top 2.6% of submissions)
A train-free framework for 3D medical volumes that led the evaluated methods on 7 of 10 datasets, processed a full volume in about 6.5 seconds on a single 11 GB GPU, and enabled brain-MRI analyses identifying 126 novel genetic associations while replicating 111 of 118 previously reported associations.