| 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 |