SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image

• URL: http://arxiv.org/abs/2307.04973v1
• Date: Tue, 11 Jul 2023 02:27:45 GMT
• Title: SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
• Authors: Guoyao Deng, Ke Zou, Kai Ren, Meng Wang, Xuedong Yuan, Sancong Ying and Huazhu Fu
• Abstract summary: We propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. Our experimental results found that multi-box prompts augmentation improve the SAM performance, and endowed each pixel with uncertainty.
• Score: 29.063412868961098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Segmenting Anything has taken an important step towards general artificial intelligence. At the same time, its reliability and fairness have also attracted great attention, especially in the field of health care. In this study, we propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. We estimate the distribution of SAM predictions via Monte Carlo with prior distribution parameters, which employs different prompts as formulation of test-time augmentation. Our experimental results found that multi-box prompts augmentation improve the SAM performance, and endowed each pixel with uncertainty. This provides the first paradigm for a reliable SAM.

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