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arXiv · 2502.03501

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation

Wang Xinyi·Kang Hongyu·Wei Peishan·Shuai Li·Yu Sun·Sai Kit Lam·Yongping Zheng
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Abstract

In this paper, we aim to address the unmet demand for automated prompting and enhanced human-model interactions of SAM and SAM2 for the sake of promoting their widespread clinical adoption. Specifically, we propose Proxy Prompt (PP), auto-generated by leveraging non-target data with a pre-annotated mask. We devise a novel 3-step context-selection strategy for adaptively selecting the most representative contextual information from non-target data via vision mamba and selective maps, empowering the guiding capability of non-target image-mask pairs for segmentation on target image/video data. To reinforce human-model interactions in PP, we further propose a contextual colorization module via a dual-reverse cross-attention to enhance interactions between target features and contextual-embedding with amplifying distinctive features of user-defined object(s). Via extensive evaluations, our method achieves state-of-the-art performance on four public datasets and yields comparable results with fully-trained models, even when trained with only 16 image masks.

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Wang Xinyi, Kang Hongyu, Wei Peishan, Shuai Li, Yu Sun, Sai Kit Lam, Yongping Zheng. 2025-02-05. Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation. https://arxiv.org/abs/2502.03501

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