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

arXiv: https://arxiv.org/abs/2405.16740 · HTML: https://mlsanity.com/paper/2405.16740/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023)
- [EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2405.06880/) (2024)
- [Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2303.16892/) (2023)
- [Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?](https://mlsanity.com/paper/2308.06623/) (2023)
- [G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation](https://mlsanity.com/paper/2310.16175/) (2023)

## Built on by (most cited first, 3 of 3)

- [Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges](https://mlsanity.com/paper/2507.09562/) (2025)
- [Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing](https://mlsanity.com/paper/2410.16732/) (2024)
- [Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2601.15734/) (2026)

## Abstract (arXiv)

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.