Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars

Understand

The Segment Anything Model (SAM) has demonstrated remarkable capabilities of scaled-up segmentation models, enabling zero-shot generalization across a variety of domains.

• By leveraging large-scale foundational models as pre-trained models, it is a natural progression to fine-tune SAM for specific domains to further enhance performances.
• However, the adoption of foundational models in the medical domain presents a challenge due to the difficulty and expense of labeling sufficient data for adaptation within hospital systems.
• In this paper, we introduce an efficient and practical approach for fine-tuning SAM using a limited number of exemplars, making it suitable for such scenarios.