Search Results for author: Quanzheng Li

Found 37 papers, 3 papers with code

Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior

no code implementations18 Jun 2021 Kuang Gong, Ciprian Catana, Jinyi Qi, Quanzheng Li

Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework.

Denoising

Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection

no code implementations21 Mar 2021 Varun Buch, Aoxiao Zhong, Xiang Li, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Dufan Wu, Hui Ren, Jiahui Guan, Andrew Liteplo, Sayon Dutta, Ittai Dayan, Quanzheng Li

Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.

A General Computational Framework to Measure the Expressiveness of Complex Networks using a Tight Upper Bound of Linear Regions

no code implementations1 Jan 2021 Yutong Xie, Gaoxiang Chen, Quanzheng Li

Inspired by the proof of this upper bound and the framework of matrix computation in \citet{hinz2019framework}, we propose a general computational approach to compute a tight upper bound of regions number for theoretically any network structures (e. g. DNN with all kind of skip connections and residual structures).

MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis

no code implementations29 Dec 2020 Mo Zhang, Quanzheng Li

It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer.

Histopathological Image Classification Image Classification

A General Computational Framework to Measure the Expressiveness of Complex Networks Using a Tighter Upper Bound of Linear Regions

no code implementations8 Dec 2020 Yutong Xie, Gaoxiang Chen, Quanzheng Li

Inspired by the proof of this upper bound and theframework of matrix computation in Hinz & Van de Geer (2019), we propose ageneral computational approach to compute a tight upper bound of regions numberfor theoretically any network structures (e. g. DNN with all kind of skip connec-tions and residual structures).

Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19

no code implementations26 Nov 2020 Aoxiao Zhong, Xiang Li, Dufan Wu, Hui Ren, Kyungsang Kim, YoungGon Kim, Varun Buch, Nir Neumark, Bernardo Bizzo, Won Young Tak, Soo Young Park, Yu Rim Lee, Min Kyu Kang, Jung Gil Park, Byung Seok Kim, Woo Jin Chung, Ning Guo, Ittai Dayan, Mannudeep K. Kalra, Quanzheng Li

These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.

Image Retrieval Metric Learning

Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

1 code implementation26 Sep 2020 Young-Gon Kim, Kyungsang Kim, Dufan Wu, Hui Ren, Won Young Tak, Soo Young Park, Yu Rim Lee, Min Kyu Kang, Jung Gil Park, Byung Seok Kim, Woo Jin Chung, Mannudeep K. Kalra, Quanzheng Li

A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs.

COVID-19 Diagnosis

Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network

no code implementations14 Sep 2020 Jianan Cui, Kuang Gong, Paul Han, Huafeng Liu, Quanzheng Li

After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.

SSIM Super-Resolution

Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network

no code implementations13 Sep 2020 Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Jianan Cui, Zhifang Wu, Huafeng Liu, Quanzheng Li

Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.

Denoising

Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks

no code implementations19 May 2020 Dufan Wu, Hui Ren, Quanzheng Li

It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis.

Image Denoising

A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks

no code implementations8 May 2020 Mengjia Xu, David Lopez Sanz, Pilar Garces, Fernando Maestu, Quanzheng Li, Dimitrios Pantazis

Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms.

Multi-label Detection and Classification of Red Blood Cells in Microscopic Images

no code implementations7 Oct 2019 Wei Qiu, Jiaming Guo, Xiang Li, Mengjia Xu, Mo Zhang, Ning Guo, Quanzheng Li

As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.

Classification General Classification +1

Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging

no code implementations1 Oct 2019 Jiaming Guo, Wei Qiu, Xiang Li, Xuandong Zhao, Ning Guo, Quanzheng Li

Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET).

Graph Clustering

Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images

no code implementations26 Jul 2019 Fei Yu, Jie Zhao, Yanjun Gong, Zhi Wang, Yuxi Li, Fan Yang, Bin Dong, Quanzheng Li, Li Zhang

Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.

Transfer Learning

ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning

no code implementations7 Jul 2019 Mo Zhang, Jie Zhao, Xiang Li, Li Zhang, Quanzheng Li

Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale.

Semantic Segmentation

Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples

no code implementations9 Jun 2019 Dufan Wu, Kuang Gong, Kyungsang Kim, Quanzheng Li

In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.

Image Denoising Medical Image Denoising

Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model

1 code implementation3 Dec 2018 Jie Zhao, Quanzheng Li, Xiang Li, Hongfeng Li, Li Zhang

Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image.

Medical Image Segmentation

Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction

no code implementations5 Oct 2018 Dufan Wu, Kyungsang Kim, Quanzheng Li

The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images.

Computed Tomography (CT) Image Reconstruction

Network Modeling and Pathway Inference from Incomplete Data ("PathInf")

no code implementations1 Oct 2018 Xiang Li, Qitian Chen, Xing Wang, Ning Guo, Nan Wu, Quanzheng Li

In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.

Data Summarization

Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

no code implementations6 Aug 2018 Jiasha Liu, Xiang Li, Hui Ren, Quanzheng Li

The framework combines two 1st-level modules: direct estimation module and a segmentation module.

Ensemble Learning

Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)

no code implementations31 May 2018 Yu Zhao, Xiang Li, Wei zhang, Shijie Zhao, Milad Makkie, Mo Zhang, Quanzheng Li, Tianming Liu

Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.

Brain Decoding

End-to-End Abnormality Detection in Medical Imaging

no code implementations ICLR 2018 Dufan Wu, Kyungsang Kim, Bin Dong, Quanzheng Li

To align the acquisition with the annotations made by radiologists in the image domain, a DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images.

Anomaly Detection Computed Tomography (CT) +1

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

no code implementations17 Dec 2017 Kuang Gong, Jaewon Yang, Kyungsang Kim, Georges El Fakhri, Youngho Seo, Quanzheng Li

With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods.

Image Reconstruction

End-to-end Lung Nodule Detection in Computed Tomography

no code implementations6 Nov 2017 Dufan Wu, Kyungsang Kim, Bin Dong, Georges El Fakhri, Quanzheng Li

With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data.

Computed Tomography (CT) Lung Nodule Detection

Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

no code implementations ICML 2018 Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong

We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations.

Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net

no code implementations23 Oct 2017 Mo Zhang, Xiang Li, Mengjia Xu, Quanzheng Li

Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.

Cell Segmentation Classification +2

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

1 code implementation9 Oct 2017 Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Georges El Fakhri, Jinyi Qi, Quanzheng Li

An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool.

Denoising Image Reconstruction

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

no code implementations21 Jul 2017 Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, Vahid Tarokh

In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation.

Denoising Dictionary Learning

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

no code implementations19 Jul 2017 Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li

However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.

Computed Tomography (CT)

Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net

no code implementations29 May 2017 Songting Shi, Xiang Li, Arkadiusz Sitek, Quanzheng Li

In this article, we derive a Bayesian model to learning the sparse and low rank PARAFAC decomposition for the observed tensor with missing values via the elastic net, with property to find the true rank and sparse factor matrix which is robust to the noise.

A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

no code implementations11 May 2017 Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li

Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT).

Computed Tomography (CT) Image Denoising

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