SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
This addresses reliability concerns for medical image segmentation applications, though it appears incremental as it builds on existing SAM architecture.
The paper tackled the reliability problem of Segment Anything Model (SAM) in medical imaging by proposing multi-box prompts triggered uncertainty estimation, which improved SAM performance and provided pixel-level uncertainty measurements.
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.