Global-Local Medical SAM Adaptor Based on Full Adaption
<|reference_start|>Global-Local Medical SAM Adaptor Based on Full Adaption: Emerging of visual language models, such as the segment anything model (SAM), have made great breakthroughs in the field of universal semantic segmentation and significantly aid the improvements of medical image segmentation, in particular with the help of Medical SAM adaptor (Med-SA). However, Med-SA still can be improved, as it fine-tunes SAM in a partial adaption manner. To resolve this problem, we present a novel global medical SAM adaptor (GMed-SA) with full adaption, which can adapt SAM globally. We further combine GMed-SA and Med-SA to propose a global-local medical SAM adaptor (GLMed-SA) to adapt SAM both globally and locally. Extensive experiments have been performed on the challenging public 2D melanoma segmentation dataset. The results show that GLMed-SA outperforms several state-of-the-art semantic segmentation methods on various evaluation metrics, demonstrating the superiority of our methods.<|reference_end|>
@article{wang2024global-local,
    title={Global-Local Medical SAM Adaptor Based on Full Adaption},
    author={Meng Wang (School of Electronic and Information Engineering Liaoning
  Technical University Xingcheng City, Liaoning Province, P. R. China), Yarong
  Feng (School of Electronic and Information Engineering Liaoning Technical
  University Xingcheng City, Liaoning Province, P. R. China), Yongwei Tang
  (School of Electronic and Information Engineering Liaoning Technical
  University Xingcheng City, Liaoning Province, P. R. China), Tian Zhang
  (Software college Northeastern University Shenyang, Liaoning Province, P. R.
  China), Yuxin Liang (School of Electronic and Information Engineering
  Liaoning Technical University Xingcheng City, Liaoning Province, P. R.
  China), Chao Lv (Department of General Surgery, Shengjing Hospital China
  Medical University Shenyang, Liaoning Province, P. R. China)},
    journal={arXiv preprint arXiv:2409.17486},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17486},
    primaryClass={cs.AI cs.CV}
}