提出全局-局部适配器，提升医学图像分割精度
Global-Local Medical SAM Adaptor Based on Full Adaption
- 全适配策略全局调整SAM，结合局部优化提升性能
- 在皮肤癌数据集上超越多个先进方法，显著提高分割准确率
- 适合医学图像分割研究者参考，尤其关注模型适配细节
视觉语言模型如分割一切模型（SAM）在通用语义分割领域取得突破，显著推动了医学图像分割的发展，尤其是借助医学SAM适配器（Med-SA）。然而，现有Med-SA仅采用部分适配方式，仍有改进空间。为此，本文提出一种全新的全局医学SAM适配器（GMed-SA），实现对SAM的全局适配；进一步将GMed-SA与Med-SA结合，提出全局-局部医学SAM适配器（GLMed-SA），实现对SAM的全局与局部双重适配。在具有挑战性的公开2D皮肤癌分割数据集上进行了大量实验，结果表明，GLMed-SA在多种评估指标上均优于多个当前最先进的语义分割方法，充分证明了该方法的优越性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。