76 citations · 187 across the 20 of their papers we have counts for
20 papers
Learning Anatomy-Grounded CT Vision-Language Representations with Organ-Hierarchical Report Knowledge
Guoliang You, Hongming Li, Yuanwang Zhang +1
Medical vision-language pretraining (VLP) from paired CT images and radiology reports enables scalable representation learning, but most existing methods align either whole scans w…
Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
Xiaoyang Chen, Hao Zheng, Yuemeng Li +4
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building s…
SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
Hao Zheng, Hongming Li, Yong Fan
To achieve fast, robust, and accurate reconstruction of the human cortical surfaces from 3D magnetic resonance images (MRIs), we develop a novel deep learning-based framework, refe…
Deep Clustering Survival Machines with Interpretable Expert Distributions
Bojian Hou, Hongming Li, Zhicheng Jiao +3
Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. I…
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…
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…
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…