2 citations · 4 across the 4 of their papers we have counts for
4 papers
Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images
Ahjol Senbi, Tianyu Huang, Fei Lyu +8
We explore the feasibility and potential of building a ground-truth-free evaluation model to assess the quality of segmentations generated by the Segment Anything Model (SAM) and i…
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
Tianyu Huang, Tao Zhou, Weidi Xie +3
The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation fo…
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Yizhe Zhang, Shuo Wang, Tao Zhou +2
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates…
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification
Yizhe Zhang, Shuo Wang, Yejia Zhang +1
Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP p…