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SemiSAM

Official repository of BIBM'24 paper "SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization".

Under construction. Coming soon.

Introduction

• Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the performance by utilizing unlabeled data, there are still gaps between fully supervised methods under extremely limited annotation scenarios.

• We propose a simple yet efficient strategy to explore the usage of the Segment Anything Model (SAM) for enhancing semi-supervised medical image segmentation. Please refer to the paper for more details.

:computer: Usage

:books: Citation

If you find this paper useful, please consider citing:

@article{SemiSAM,
  title={SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization},
  author={Zhang, Yichi and Yang, Jin and Liu, Yuchen and Cheng, Yuan and Qi, Yuan},
  journal={arXiv preprint arXiv:2312.06316},
  year={2023}
}