REVIEW 2 cited by
SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 2 Pith papers
-
SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation
SPA presents users with four representative segmentation candidates, and a learned mixture-of-Gaussians preference distribution updates from the user's selection to converge to their preferred boundary in a few interactions.
-
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
A structured survey of prompt engineering methods for the Segment Anything Model, covering geometric, textual, and multimodal prompts and their applications.
Discussion (0). Continue with ORCID to comment.