PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation

TL;DR

This paper introduces PP-SAM, a robust fine-tuning method for the Segment Anything Model that uses perturbed prompts to improve polyp segmentation accuracy with limited data, outperforming existing methods.

Contribution

We propose a novel perturbation-based fine-tuning technique, PP-SAM, that enhances SAM's robustness for polyp segmentation with minimal training samples.

Findings

1-shot fine-tuning improves DICE score by up to 37%.

Perturbed prompts significantly increase model resilience during inference.

PP-SAM outperforms recent state-of-the-art methods in polyp segmentation.

Abstract

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.