65 citations · 71 across the 2 of their papers we have counts for
3 papers
eess.IV2025
Relation U-Net
Sheng He, Rina Bao, P. Ellen Grant +1
Towards clinical interpretations, this paper presents a new ''output-with-confidence'' segmentation neural network with multiple input images and multiple output segmentation maps…
eess.IV2023★ 65 cited
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Sheng He, Rina Bao, Jingpeng Li +4
Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes with…
eess.IV2023★ 6 cited
U-Netmer: U-Net meets Transformer for medical image segmentation
Sheng He, Rina Bao, P. Ellen Grant +1
The combination of the U-Net based deep learning models and Transformer is a new trend for medical image segmentation. U-Net can extract the detailed local semantic and texture inf…