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---
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](https://huggingface.co/papers/2503.16055)
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](https://huggingface.co/datasets/pythn/ROSE) (Retinal OCT Angiography)
- [ARCADE](https://huggingface.co/datasets/pythn/ARCADE) (Coronary Artery Segmentation)
- [DRIVE](https://huggingface.co/datasets/pythn/drive) (Retinal Vessel Segmentation)
- [DIAS](https://huggingface.co/datasets/pythn/DIAS) (Dynamic Digital Subtraction Angiography)
- [Xray-Angio](https://huggingface.co/datasets/pythn/DB) (Occluded Vessel Segmentation)
Code: https://github.com/YourUsername/SALT.git |