WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
arxiv(2024)
摘要
The Segment Anything Model (SAM) marks a significant advancement in
segmentation models, offering powerful zero-shot capabilities 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 original prompt-driven design, efficiency, and
zero-shot adaptability. To fully exploit pretrained knowledge while minimizing
training overhead, we keep SAM frozen, only introducing minimal additional
parameters and computation. 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要