RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation
RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation explores RPG-SAM is a training-free one-shot polyp segmentation framework that leverages reliability-weighted prototypes and geometric adaptive selection to improve segmentation accuracy, offering a scalable alternative to expert annotations.. Commercial viability score: 7/10 in Medical AI.
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