  The Segment Anything Model (SAM), originally designed for general-purpose
segmentation tasks, has been used recently for polyp segmentation. Nonetheless,
fine-tuning SAM with data from new imaging centers or clinics poses significant
challenges. This is because this necessitates the creation of an expensive and
time-intensive annotated dataset, along with the potential for variability in
user prompts during inference. To address these issues, we propose a robust
fine-tuning technique, PP-SAM, that allows SAM to adapt to the polyp
segmentation task with limited images. To this end, we utilize variable
perturbed bounding box prompts (BBP) to enrich the learning context and enhance
the model's robustness to BBP perturbations during inference. Rigorous
experiments on polyp segmentation benchmarks reveal that our variable BBP
perturbation significantly improves model resilience. Notably, on Kvasir,
1-shot fine-tuning boosts the DICE score by 20% and 37% with 50 and 100-pixel
BBP perturbations during inference, respectively. Moreover, our experiments
show that 1-shot, 5-shot, and 10-shot PP-SAM with 50-pixel perturbations during
inference outperform a recent state-of-the-art (SOTA) polyp segmentation method
by 26%, 7%, and 5% DICE scores, respectively. Our results motivate the broader
applicability of our PP-SAM for other medical imaging tasks with limited
samples. Our implementation is available at https://github.com/SLDGroup/PP-SAM.
