2024-12-23-shaharabany24a.md

title	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
Zero-Shot Medical Image Segmentation Based on Sparse Prompt Using Finetuned SAM	Segmentation of medical images plays a critical role in various clinical applications, facilitat- ing precise diagnosis, treatment planning, and disease monitoring. However, the scarcity of annotated data poses a significant challenge for training deep learning models in the medical imaging domain. In this paper, we propose a novel approach for minimally-guided zero-shot segmentation of medical images using the Segment Anything Model (SAM), orig- inally trained on natural images. The method leverages SAM’s ability to segment arbitrary objects in natural scenes and adapts it to the medical domain without the need for labeled medical data, except for a few foreground and background points on the test image it- self. To this end, we introduce a two-stage process, involving the extraction of an initial mask from self-similarity maps and test-time fine-tuning of SAM. We run experiments on diverse medical imaging datasets, including AMOS22, MoNuSeg and the Gland segmen- tation (GlaS) challenge, and demonstrate the effectiveness of our approach.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	shaharabany24a	0	Zero-Shot Medical Image Segmentation Based on Sparse Prompt Using Finetuned SAM	1387	1400	1387-1400	1387	false	Shaharabany, Tal and Wolf, Lior	given family Tal Shaharabany given family Lior Wolf	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/shaharabany24a/shaharabany24a.pdf