@inproceedings{
yuzhihuang2024psam,
title={P{\textasciicircum}2{SAM}: Probabilistically Prompted {SAM}s Are Efficient Segmentator for Ambiguous Medical Images},
author={Yuzhihuang and Chenxin Li and ZiXu Lin and Hengyu Liu and haote xu and Yifan Liu and Yue Huang and Xinghao Ding and Xiaotong Tu and Yixuan Yuan},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=IjULktl95t}
}
The ability to predict multiple potential outputs for a single input can significantly address visual ambiguity, such as diverse semantic segmentation annotations for a medical image provided by different experts. Existing methods employ various advanced probabilistic modeling techniques to model the ambiguous prediction, while they often struggle to fit the underlying distribution for multiple outputs when only a limited number of ambiguously labeled data is available, which is usually the case in real-world applications. To overcome the challenges, we propose a framework  that leverages the prior knowledge from foundation models during segmenting ambiguous objects., termed as P² SAM. We delve into an inherent disadvantage of SAM, i.e., the sensitivity of the output to prompts, and ingeniously transform it into an advantage on ambiguous segmentation  in turn by introducing a prompt generation module. Experimental results demonstrate that by utilizing only a small number of doctor-annotated ambiguous samples, our strategy significantly enhances the precision and diversity for medical segmentation. In rigorous benchmarking experiments against cutting-edge methods, our method achieves increased segmentation precision and diversified outputs with even fewer training data (5.5% sample, +12% $D_{max}$). P² SAM signifies a steady step towards the practical deployment of  probabilistic models in real-world data-limited scenarios.
P^2SAM: Probabilistically Prompted SAMs Are Efficient Segmentator for Ambiguous Medical Images
Yuzhihuang Chenxin Li ZiXu Lin Hengyu Liu haote xu Yifan Liu Yue Huang Xinghao Ding Xiaotong Tu Yixuan Yuan