Congrats to Debesh! His CVPR 2024 Workshop paper was accepted!

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. 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. Our results motivate the broader applicability of our PP-SAM for other medical imaging tasks with limited samples.