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DINOv2-based-Self-Supervised-Learning

This work was accepted into ISBI 2024. Link to our paper.

Abstract

Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their adaptability to unforeseen categories remains a challenge. Few-shot segmentation (FSS) offers a promising solution by endowing models with the capacity to learn novel classes from limited labeled examples. A leading method for FSS is ALPNet, which compares features between the query im- age and the few available support segmented images. A key question about using ALPNet is how to design its features. In this work 1 , we delve into the potential of using features from DINOv2, which is a foundational self-supervised learn- ing model in computer vision. Leveraging the strengths of ALPNet and harnessing the feature extraction capabilities of DINOv2, we present a novel approach to few-shot segmen- tation that not only enhances performance but also paves the way for more robust and adaptable medical image analysis.

How To Run

1. Data preprocessing

Please see the notebook data/data_processing.ipynb for instructions. For convenience i've compiled the data processing instruction from the instructions form the https://github.com/cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation to a single notebook. The ct dataset is available here: https://www.synapse.org/Synapse:syn3553734

The MRI dataset is availabel here: https://chaos.grand-challenge.org

run ./data/CHAOST2/dcm_img_to_nii.sh to convert dicom images to nifti files.

2. Training and Validation

./main.sh [MODE] [MODALITY] [LABEL_SET]

MODE - validation or training
MODALITY - ct or mri
LABEL_SET - 0 (kidneys), 1 (liver spleen)

for example:

./main.sh training mri 1

Please refer to main.sh for further configurations.

Acknowledgements

This work is largely based on ALPNet and DINOv2.

Cite

If you found this repo useful, please consider giving us a citation and a star!

@misc{ayzenberg2024dinov2,
      title={DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation}, 
      author={Lev Ayzenberg and Raja Giryes and Hayit Greenspan},
      year={2024},
      eprint={2403.03273},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}