metadata
task_categories:
- image-segmentation
This repository contains the datasets used in the paper SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation
SALT is a novel Parameter-Efficient Fine-Tuning (PEFT) method designed to adapt large-scale foundation models—especially Segment Anything Model (SAM)—to domain-specific tasks such as medical image segmentation.
The following datasets are used:
- ROSE (Retinal OCT Angiography)
- ARCADE (Coronary Artery Segmentation)
- DRIVE (Retinal Vessel Segmentation)
- DIAS (Dynamic Digital Subtraction Angiography)
- Xray-Angio (Occluded Vessel Segmentation)