Search Results for author: Quanzheng Li

Found 78 papers, 14 papers with code

Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study

no code implementations9 May 2024 Tianpeng Zhang, Sekeun Kim, Jerome Charton, Haitong Ma, Kyungsang Kim, Na Li, Quanzheng Li

The paper introduces a novel autonomous robot ultrasound (US) system targeting liver follow-up scans for outpatients in local communities.

Computed Tomography (CT) Navigate

Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis

no code implementations9 Apr 2024 Sekeun Kim, Hui Ren, Peng Guo, Abder-Rahman Ali, Patrick Zhang, Kyungsang Kim, Xiang Li, Quanzheng Li

Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various standard views.

Language Modelling Segmentation

AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information

no code implementations19 Mar 2024 Jiang Hu, Quanzheng Li

Our key observation is that the associated generalized Fisher information matrix is either low-rank or extremely small-scaled.

Cardiac Magnetic Resonance 2D+T Short- and Long-axis Segmentation via Spatio-temporal SAM Adaptation

no code implementations15 Mar 2024 Zhennong Chen, Sekeun Kim, Hui Ren, Quanzheng Li, Xiang Li

Accurate 2D+T myocardium segmentation in cine cardiac magnetic resonance (CMR) scans is essential to analyze LV motion throughout the cardiac cycle comprehensively.

Myocardium Segmentation Segmentation +1

Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting

no code implementations11 Mar 2024 WenTing Chen, Pengyu Wang, Hui Ren, Lichao Sun, Quanzheng Li, Yixuan Yuan, Xiang Li

To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.

Anatomy Descriptive +1

Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction

no code implementations11 Mar 2024 Qing Xiao, Siyeop Yoon, Hui Ren, Matthew Tivnan, Lichao Sun, Quanzheng Li, Tianming Liu, Yu Zhang, Xiang Li

Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to its progression.

Trajectory Prediction

Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising

no code implementations10 Mar 2024 Yuang Wang, Siyeop Yoon, Pengfei Jin, Matthew Tivnan, Zhennong Chen, Rui Hu, Li Zhang, Zhiqiang Chen, Quanzheng Li, Dufan Wu

As a promising alternative, the Image-to-Image Schr\"odinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models.

Denoising Image Restoration +1

The Radiation Oncology NLP Database

1 code implementation19 Jan 2024 Zhengliang Liu, Jason Holmes, Wenxiong Liao, Chenbin Liu, Lian Zhang, Hongying Feng, Peilong Wang, Muhammad Ali Elahi, Hongmin Cai, Lichao Sun, Quanzheng Li, Xiang Li, Tianming Liu, Jiajian Shen, Wei Liu

ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.

Language Modelling Large Language Model +7

MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

1 code implementation16 Sep 2023 Cheng Chen, Juzheng Miao, Dufan Wu, Zhiling Yan, Sekeun Kim, Jiang Hu, Aoxiao Zhong, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.

Image Segmentation Medical Image Segmentation +4

CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study

no code implementations21 Jul 2023 Zihan Guan, Zihao Wu, Zhengliang Liu, Dufan Wu, Hui Ren, Quanzheng Li, Xiang Li, Ninghao Liu

Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research.

Few-Shot Learning text-classification +1

Segment Anything Model (SAM) for Radiation Oncology

no code implementations20 Jun 2023 Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, Dajiang Zhu, Tianming Liu, Wei Liu

Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.


AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

no code implementations16 Jun 2023 Haixing Dai, Yiwei Li, Zhengliang Liu, Lin Zhao, Zihao Wu, Suhang Song, Ye Shen, Dajiang Zhu, Xiang Li, Sheng Li, Xiaobai Yao, Lu Shi, Quanzheng Li, Zhuo Chen, Donglan Zhang, Gengchen Mai, Tianming Liu

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.

Language Modelling Large Language Model

Artificial General Intelligence for Medical Imaging

no code implementations8 Jun 2023 Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu, Dinggang Shen

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

no code implementations18 Apr 2023 Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Haixing Dai, Chong Ma, Zhengliang Liu, Lin Zhao, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.

Specificity Task 2

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

1 code implementation20 Mar 2023 Zhengliang Liu, Yue Huang, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Yiwei Li, Peng Shu, Fang Zeng, Lichao Sun, Wei Liu, Dinggang Shen, Quanzheng Li, Tianming Liu, Dajiang Zhu, Xiang Li

The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.

Benchmarking De-identification +4

Decentralized Riemannian natural gradient methods with Kronecker-product approximations

no code implementations16 Mar 2023 Jiang Hu, Kangkang Deng, Na Li, Quanzheng Li

With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problems.

SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images

no code implementations8 Feb 2023 Gary Y. Li, Junyu Chen, Se-In Jang, Kuang Gong, Quanzheng Li

Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions. To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR.

Image Segmentation Segmentation +1

Diffusion Model for Generative Image Denoising

no code implementations5 Feb 2023 Yutong Xie, Minne Yuan, Bin Dong, Quanzheng Li

In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model.

Image Denoising

Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation

no code implementations21 Dec 2022 Ye Li, Junyu Chen, Se-In Jang, Kuang Gong, Quanzheng Li

Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation.

Segmentation Tumor Segmentation

PET image denoising based on denoising diffusion probabilistic models

no code implementations13 Sep 2022 Kuang Gong, Keith A. Johnson, Georges El Fakhri, Quanzheng Li, Tinsu Pan

Regional and surface quantification shows that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference can achieve the best performance.

Image Denoising

FedDAR: Federated Domain-Aware Representation Learning

no code implementations8 Sep 2022 Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li

To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients.

Federated Learning Representation Learning

Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising

1 code implementation7 Sep 2022 Se-In Jang, Tinsu Pan, Ye Li, Pedram Heidari, Junyu Chen, Quanzheng Li, Kuang Gong

In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs.

Decoder Image Denoising

A Noise-level-aware Framework for PET Image Denoising

no code implementations15 Mar 2022 Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se-In Jang, Kyungsang Kim, Kuang Gong, Quanzheng Li

Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.

Image Denoising SSIM

Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction

1 code implementation5 Mar 2022 Yutong Xie, Quanzheng Li

We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM.

Denoising MRI Reconstruction

Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer

1 code implementation8 Feb 2022 Yutong Xie, Dufan Wu, Bin Dong, Quanzheng Li

We proved that a trained model in supervised deep learning minimizes the conditional risk for each input (Theorem 2. 1).

Image Super-Resolution

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.


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 Management +2

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 Segmentation

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.

Generative Adversarial Network SSIM +1

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.


Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography

no code implementations22 May 2020 Dufan Wu, Daniel Montes, Ziheng Duan, Yangsibo Huang, Javier M. Romero, Ramon Gilberto Gonzalez, Quanzheng Li

Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network.

Region Proposal Specificity

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.

Cell Detection Classification +2

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).

Clustering 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.

Generative Adversarial Network 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.

Image Segmentation Medical Image Segmentation +2

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 Management

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) +2

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 +4

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) Lesion Detection

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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