# RPG-SAM

The work proposes RPG-SAM, a SAM2-based training-free one-shot polyp segmentation framework that uses Reliability-Weighted Prototype Mining (RWPM) to weight support foreground prototypes by a contrast factor and a reverse purity factor while using background prototypes as negative anchors for noise suppression, Geometric Adaptive Selection (GAS) to pick binarization thresholds dynamically from morphological solidity and scale consensus, and a Prior-guided Iterative Refinement (PIR) loop to polish boundaries, reaching 78.65% mIoU and 85.65% mDice on Kvasir, surpassing ProtoSAM by 5.56% and 4.11% respectively, with comparisons also reported on three-center PolypGen, CVC-ClinicDB, and CVC-ColonDB.

The work proposes RPG-SAM, a SAM2-based training-free one-shot polyp segmentation framework that uses Reliability-Weighted Prototype Mining (RWPM) to weight support foreground prototypes by a contrast factor and a reverse purity factor while using background prototypes as negative anchors for noise suppression, Geometric Adaptive Selection (GAS) to pick binarization thresholds dynamically from morphological solidity and scale consensus, and a Prior-guided Iterative Refinement (PIR) loop to polish boundaries, reaching 78.65% mIoU and 85.65% mDice on Kvasir, surpassing ProtoSAM by 5.56% and 4.11% respectively, with comparisons also reported on three-center PolypGen, CVC-ClinicDB, and CVC-ColonDB.