2024-11-17-liu24a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images	Proceedings of the MICCAI Workshop on Computational Pathology	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 data set). The code will be available at https://github.com/HongLiuuuuu/WSI-SAM.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	liu24a	0	WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images	25	37	25-37	25	false	Liu, Hong and Yang, Haosen and Diest, Paul J. van and Pluim, Josien P.W. and Veta, Mitko	given family Hong Liu given family Haosen Yang given family Paul J. van Diest given family Josien P.W. Pluim given family Mitko Veta	2024-11-17		Proceedings of the MICCAI Workshop on Computational Pathology	254	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v254/main/assets/liu24a/liu24a.pdf