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The Segment Anything Model (SAM) has demonstrated remarkable capabilities ofscaled-up segmentation models, enabling zero-shot generalization across avariety of domains. By leveraging large-scale foundational models aspre-trained models, it is a natural progression to fine-tune SAM for specificdomains to further enhance performances. However, the adoption of foundationalmodels in the medical domain presents a challenge due to the difficulty andexpense of labeling sufficient data for adaptation within hospital systems. Inthis paper, we introduce an efficient and practical approach for fine-tuningSAM using a limited number of exemplars, making it suitable for such scenarios.Our approach combines two established techniques from the literature: anexemplar-guided synthesis module and the widely recognized Low-Rank Adaptation(LoRA) fine-tuning strategy, serving as data-level and model-level attemptsrespectively. Interestingly, our empirical findings suggest that SAM can beeffectively aligned within the medical domain even with few labeled data. Wevalidate our approach through experiments on brain tumor segmentation (BraTS)and multi-organ CT segmentation (Synapse). The comprehensive results underscorethe feasibility and effectiveness of such an approach, paving the way for thepractical application of SAM in the medical domain.

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