SAM-U: Multi-box prompts triggered uncertainty estimation for reliable
  SAM in medical image
Guoyao Deng Ke Zou Kai Ren Meng Wang Xuedong Yuan Sancong Ying Huazhu Fu
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.