826 citations · 1k across the 22 of their papers we have counts for
22 papers
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Vishnu M. Bashyam, Jimit Doshi, Guray Erus +24
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and…
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…
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…
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…
Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks
Hongming Li, Yong Fan
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal…
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer
Hongming Li, Pamela Boimel, James Janopaul-Naylor +4
Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learni…
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks
Shi Yin, Qinmu Peng, Hongming Li +5
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automa…
Brain decoding from functional MRI using long short-term memory recurrent neural networks
Hongming Li, Yong Fan
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging stud…
Identification of temporal transition of functional states using recurrent neural networks from functional MRI
Hongming Li, Yong Fan
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes. Besides sliding window…
Identification of multi-scale hierarchical brain functional networks using deep matrix factorization
Hongming Li, Xiaofeng Zhu, Yong Fan
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organizatio…
Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks
Hongming Li, Theodore D. Satterthwaite, Yong Fan
Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon fun…
Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data
Hongming Li, Yong Fan
A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registe…
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
Qiang Zheng, Gregory Tasian, Yong Fan
Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable…
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
Qiang Zheng, Yong Fan
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recogn…
Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data
Hongming Li, Mohamad Habes, Yong Fan
Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been focusing on…
Non-rigid image registration using fully convolutional networks with deep self-supervision
Hongming Li, Yong Fan
We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of imag…
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
Qiang Zheng, Steven Warner, Gregory Tasian +1
Purpose: To improve kidney segmentation in clinical ultrasound (US) images, we develop a new graph cuts based method to segment kidney US images by integrating original image inten…
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Xiaomei Zhao, Yihong Wu, Guidong Song +3
Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learnin…