3 citations · 7 across the 3 of their papers we have counts for
6 papers
Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging
Tianming Du, Honggang Zhang, Yuemeng Li +2
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep l…
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
Yuemeng Li, Hongming Li, Yong Fan
Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from hig…
Context-endcoding for neural network based skull stripping in magnetic resonance imaging
Zhen Liu, Borui Xiao, Yuemeng Li +1
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images. A lot of deep learn-ing neural network based methods have been developed toac…
Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation
Yuemeng Li, Hangfan Liu, Hongming Li +1
Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take…
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
Yuemeng Li, Yong Fan
Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep lear…
A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
Bo Zhou, Yuemeng Li, Jiangcong Wang
We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medi…