223 citations · 382 across the 8 of their papers we have counts for
8 papers
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…
Segmentation Ability Map: Interpret deep features for medical image segmentation
Sheng He, Yanfang Feng, P. Ellen Grant +1
Deep convolutional neural networks (CNNs) have been widely used for medical image segmentation. In most studies, only the output layer is exploited to compute the final segmentatio…
Deep Relation Learning for Regression and Its Application to Brain Age Estimation
Sheng He, Yanfang Feng, P. Ellen Grant +1
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, w…
Global-Local Transformer for Brain Age Estimation
Sheng He, P. Ellen Grant, Yangming Ou
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information…
Brain Age Estimation Using LSTM on Children's Brain MRI
Sheng He, Randy L. Gollub, Shawn N. Murphy +6
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as…