76 citations · 178 across the 11 of their papers we have counts for
16 papers
Unsupervised deep learning for individualized brain functional network identification
Hongming Li, Yong Fan
A novel unsupervised deep learning method is developed to identify individual-specific large scale brain functional networks (FNs) from resting-state fMRI (rsfMRI) in an end-to-end…
MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision
Hongming Li, Yong Fan
We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a…
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…
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…
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI
Hongming Li, Mohamad Habes, David A. Wolk +1
Introduction: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's dise…
Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network
Shi Yin, Zhengqiang Zhang, Hongming Li +5
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic m…