Search Results for author: Hongming Li

Found 22 papers, 5 papers with code

Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

no code implementations17 Nov 2023 Xiaoyang Chen, Hao Zheng, Yuemeng Li, Yuncong Ma, Liang Ma, Hongming Li, Yong Fan

A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance.

Image Segmentation Medical Image Segmentation +3

SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images

no code implementations6 Mar 2023 Hao Zheng, Hongming Li, Yong Fan

Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependence between the inner and outer surfaces, SurfNN reconstructs both the inner and outer cortical surfaces jointly by training a single network to predict a midthickness surface that lies at the center of the inner and outer cortical surfaces.

Surface Reconstruction

Deep Clustering Survival Machines with Interpretable Expert Distributions

1 code implementation27 Jan 2023 BoJian Hou, Hongming Li, Zhicheng Jiao, Zhen Zhou, Hao Zheng, Yong Fan

We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions.

Clustering Deep Clustering +1

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making

no code implementations21 Jan 2023 Shujian Yu, Hongming Li, Sigurd Løkse, Robert Jenssen, José C. Príncipe

In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples.

Decision Making Time Series +1

Causal Recurrent Variational Autoencoder for Medical Time Series Generation

1 code implementation16 Jan 2023 Hongming Li, Shujian Yu, Jose Principe

We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process.

Causal Inference EEG +3

Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing

1 code implementation7 Feb 2022 Hongming Li, Shujian Yu, Jose C. Principe

We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components.

Hyperspectral Unmixing

Unsupervised deep learning for individualized brain functional network identification

no code implementations11 Dec 2020 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 learning fashion.

Representation Learning

MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision

no code implementations4 Oct 2020 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 self-supervised learning setting.

Image Registration Self-Supervised Learning

ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation

1 code implementation13 Feb 2020 Yuemeng Li, Hongming Li, Yong Fan

However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.

Computational Efficiency Segmentation +1

Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation

no code implementations7 May 2019 Yuemeng Li, Hangfan Liu, Hongming Li, Yong Fan

In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation.

Segmentation

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI

no code implementations15 Apr 2019 Hongming Li, Mohamad Habes, David A. Wolk, Yong Fan

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 disease (AD) dementia.

Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks

no code implementations5 Jan 2019 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 elders.

General Classification

Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer

no code implementations5 Jan 2019 Hongming Li, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef, Yong Fan

To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model.

Survival Analysis Survival Prediction

Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network

no code implementations5 Jan 2019 Shi Yin, Zhengqiang Zhang, Hongming Li, Qinmu Peng, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan

It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.

Classification Distance regression +2

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

no code implementations12 Nov 2018 Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan

It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.

Classification Data Augmentation +3

Identification of temporal transition of functional states using recurrent neural networks from functional MRI

no code implementations14 Sep 2018 Hongming Li, Yong Fan

Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes.

Anomaly Detection Change Point Detection

Brain decoding from functional MRI using long short-term memory recurrent neural networks

no code implementations14 Sep 2018 Hongming Li, Yong Fan

Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies.

Brain Decoding

Identification of multi-scale hierarchical brain functional networks using deep matrix factorization

no code implementations14 Sep 2018 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 organization from resting state fMRI data.

Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks

no code implementations11 Jan 2018 Hongming Li, Theodore D. Satterthwaite, Yong Fan

Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance.

Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data

no code implementations11 Jan 2018 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 registered in a self-supervised learning framework.

Image Registration Self-Supervised Learning

Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data

no code implementations5 Sep 2017 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.

Binary Classification Classification +1

Non-rigid image registration using fully convolutional networks with deep self-supervision

1 code implementation4 Sep 2017 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 images to be registered.

Image Registration

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