Global-Local Medical SAM Adaptor Based on Full Adaption
This work addresses medical image segmentation for healthcare applications, but it is incremental as it builds on existing adaptors like Med-SA.
The authors tackled the problem of improving medical image segmentation by proposing a global-local adaptor for the Segment Anything Model (SAM), which outperformed state-of-the-art methods on a 2D melanoma segmentation dataset.
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.