9 citations · 15 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury
Rina Bao, Sheng He, Ellen Grant +1
Hypoxic-Ischemic Encephalopathy (HIE) affects 1 to 5 out of every 1,000 newborns, with 30% to 50% of cases resulting in adverse neurocognitive outcomes. However, these outcomes can…
Foundation AI Model for Medical Image Segmentation
Rina Bao, Erfan Darzi, Sheng He +6
Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high acc…
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…
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…