  The Segment Anything Model (SAM) marks a significant advancement in
segmentation models, offering robust zero-shot abilities and dynamic prompting.
However, existing medical SAMs are not suitable for the multi-scale nature of
whole-slide images (WSIs), restricting their effectiveness. To resolve this
drawback, we present WSI-SAM, enhancing SAM with precise object segmentation
capabilities for histopathology images using multi-resolution patches, while
preserving its efficient, prompt-driven design, and zero-shot abilities. To
fully exploit pretrained knowledge while minimizing training overhead, we keep
SAM frozen, introducing only minimal extra parameters and computational
overhead. In particular, we introduce High-Resolution (HR) token,
Low-Resolution (LR) token and dual mask decoder. This decoder integrates the
original SAM mask decoder with a lightweight fusion module that integrates
features at multiple scales. Instead of predicting a mask independently, we
integrate HR and LR token at intermediate layer to jointly learn features of
the same object across multiple resolutions. Experiments show that our WSI-SAM
outperforms state-of-the-art SAM and its variants. In particular, our model
outperforms SAM by 4.1 and 2.5 percent points on a ductal carcinoma in situ
(DCIS) segmentation tasks and breast cancer metastasis segmentation task
(CAMELYON16 dataset). The code will be available at
https://github.com/HongLiuuuuu/WSI-SAM.
