FS-MedSAM2: Exploring the Potential of SAM2 for Few-Shot Medical Image Segmentation without Fine-tuning
<|reference_start|>FS-MedSAM2: Exploring the Potential of SAM2 for Few-Shot Medical Image Segmentation without Fine-tuning: The Segment Anything Model 2 (SAM2) has recently demonstrated exceptional performance in zero-shot prompt segmentation for natural images and videos. However, it faces significant challenges when applied to medical images. Since its release, many attempts have been made to adapt SAM2's segmentation capabilities to the medical imaging domain. These efforts typically involve using a substantial amount of labeled data to fine-tune the model's weights. In this paper, we explore SAM2 from a different perspective via making the full use of its trained memory attention module and its ability of processing mask prompts. We introduce FS-MedSAM2, a simple yet effective framework that enables SAM2 to achieve superior medical image segmentation in a few-shot setting, without the need for fine-tuning. Our framework outperforms the current state-of-the-arts on two publicly available medical image datasets. The code is available at https://github.com/DeepMed-Lab-ECNU/FS_MedSAM2.<|reference_end|>
@article{bai2024fs-medsam2:,
    title={FS-MedSAM2: Exploring the Potential of SAM2 for Few-Shot Medical Image
  Segmentation without Fine-tuning},
    author={Yunhao Bai, Qinji Yu, Boxiang Yun, Dakai Jin, Yingda Xia, Yan Wang},
    journal={arXiv preprint arXiv:2409.04298},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04298},
    primaryClass={cs.CV}
}