Search Results for author: Ge Wang

Found 76 papers, 21 papers with code

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings

no code implementations ACL 2022 Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Li

Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.

Word Embeddings

Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection

no code implementations30 Apr 2022 Chuang Niu, Ge Wang

To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images.

Computed Tomography (CT) Contrastive Learning +1

Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection

no code implementations25 Apr 2022 Ge Wang, Li Tan, Ziliang Shang, He Liu

In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge.

Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT

no code implementations29 Mar 2022 Wenjun Xia, Hongming Shan, Ge Wang, Yi Zhang

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging.

Denoising

X-ray Dissectography Improves Lung Nodule Detection

no code implementations24 Mar 2022 Chuang Niu, Giridhar Dasegowda, Pingkun Yan, Mannudeep K. Kalra, Ge Wang

Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect.

Lung Nodule Detection

Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain

no code implementations22 Mar 2022 Rodrigo de Barros Vimieiro, Chuang Niu, Hongming Shan, Lucas Rodrigues Borges, Ge Wang, Marcelo Andrade da Costa Vieira

To accurately control the network operation point, in terms of noise and blur of the restored image, we propose a loss function that minimizes the bias and matches residual noise between the input and the output.

Medical Diagnosis

Decoupled Mixup for Data-efficient Learning

1 code implementation21 Mar 2022 Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li

Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data.

Data Augmentation Semi-Supervised Image Classification

AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis

no code implementations23 Dec 2021 Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò, Tim Leiner, Pier Luigi Dragotti, Ge Wang, Daniel Rueckert, David Firmin, Guang Yang

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.

Phase function estimation from a diffuse optical image via deep learning

no code implementations16 Nov 2021 Yuxuan Liang, Chuang Niu, Chen Wei, Shenghan Ren, Wenxiang Cong, Ge Wang

The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters.

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

1 code implementation12 Nov 2021 Hongming Shan, Rodrigo de Barros Vimieiro, Lucas Rodrigues Borges, Marcelo Andrade da Costa Vieira, Ge Wang

Results showed that the perceptual loss function (PL4) is able to achieve virtually the same noise levels of a full-dose acquisition, while resulting in smaller signal bias compared to other loss functions.

On Expressivity and Trainability of Quadratic Networks

1 code implementation12 Oct 2021 Feng-Lei Fan, Mengzhou Li, Fei Wang, Rongjie Lai, Ge Wang

Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed.

Debiased Graph Contrastive Learning

no code implementations5 Oct 2021 Jun Xia, Lirong Wu, Jintao Chen, Ge Wang, Stan Z. Li

We find that many hard negative samples similar to anchor point are false negative ones (samples from the same class as anchor point) in GCL, which is different from CL in computer vision and will lead to unsatisfactory performance of existing hard negative mining techniques in GCL.

Contrastive Learning Representation Learning

Disrupting Adversarial Transferability in Deep Neural Networks

1 code implementation27 Aug 2021 Christopher Wiedeman, Ge Wang

Furthermore, we show how applying a feature correlation loss, which decorrelates the extracted features in a latent space, can reduce the transferability of adversarial attacks between models, suggesting that the models complete tasks in semantically different ways.

Adversarial Attack

Stationary Multi-source AI-powered Real-time Tomography (SMART)

no code implementations27 Aug 2021 Weiwen Wu, Yaohui Tang, Tianling Lv, Chuang Niu, Cheng Wang, Yiyan Guo, Yunheng Chang, Ge Wang, Yan Xi

The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac micro-CT with the unprecedented temporal resolution of 30ms, which is an order of magnitude higher than the state of the art.

Computed Tomography (CT)

Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing

no code implementations ACL 2021 Liwen Zhang, Ge Wang, Wenjuan Han, Kewei Tu

In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing.

Dependency Parsing Discourse Parsing

Cross-modal Attention for MRI and Ultrasound Volume Registration

1 code implementation9 Jul 2021 Xinrui Song, Hengtao Guo, Xuanang Xu, Hanqing Chao, Sheng Xu, Baris Turkbey, Bradford J. Wood, Ge Wang, Pingkun Yan

In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.

Image Registration

AI-Enabled Ultra-Low-Dose CT Reconstruction

no code implementations17 Jun 2021 Weiwen Wu, Chuang Niu, Shadi Ebrahimian, Hengyong Yu, Mannu Kalra, Ge Wang

By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children.

GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification

no code implementations29 Apr 2021 HaoYuan Chen, Chen Li, Ge Wang, Xiaoyan Li, Md Rahaman, Hongzan Sun, Weiming Hu, Yixin Li, Wanli Liu, Changhao Sun, Shiliang Ai, Marcin Grzegorzek

Finally, a comparative study is performed to test the generalizability with both H&E and immunohistochemical stained images on a lymphoma image dataset and a breast cancer dataset, producing comparable F1-scores (85. 6% and 82. 8%) and accuracies (83. 9% and 89. 4%), respectively.

Adversarial Attack General Classification +2

Task-Oriented Low-Dose CT Image Denoising

1 code implementation25 Mar 2021 Jiajin Zhang, Hanqing Chao, Xuanang Xu, Chuang Niu, Ge Wang, Pingkun Yan

The extensive use of medical CT has raised a public concern over the radiation dose to the patient.

Image Denoising

Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning

no code implementations18 Mar 2021 Arjun Krishna, Kedar Bartake, Chuang Niu, Ge Wang, Youfang Lai, Xun Jia, Klaus Mueller

Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics.

Data Augmentation Image Generation +3

SPICE: Semantic Pseudo-labeling for Image Clustering

1 code implementation17 Mar 2021 Chuang Niu, Hongming Shan, Ge Wang

In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy.

Contrastive Learning Deep Clustering +4

Noise Entangled GAN For Low-Dose CT Simulation

no code implementations18 Feb 2021 Chuang Niu, Ge Wang, Pingkun Yan, Juergen Hahn, Youfang Lai, Xun Jia, Arjun Krishna, Klaus Mueller, Andreu Badal, KyleJ. Myers, Rongping Zeng

We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image.

Computed Tomography (CT)

Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding

no code implementations COLING 2020 Ge Wang, Kewei Tu

By relaxing the tree constraint in both the encoder and the decoder during training, we make the learning of our model fully arc-factored and thus circumvent the challenges brought by the tree constraint.

Dependency Parsing

Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning

1 code implementation6 Nov 2020 Chuang Niu, Mengzhou Li, Fenglei Fan, Weiwen Wu, Xiaodong Guo, Qing Lyu, Ge Wang

Limited by the independent noise assumption, current unsupervised denoising methods cannot process correlated noises as in CT images.

Computed Tomography (CT) Image Denoising

Deep Learning based Spectral CT Imaging

no code implementations28 Aug 2020 Weiwen Wu, Dianlin Hu, Chuang Niu, Lieza Vanden Broeke, Anthony P. H. Butler, Peng Cao, James Atlas, Alexander Chernoglazov, Varut Vardhanabhuti, Ge Wang

To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods.

Computed Tomography (CT) Deblurring +2

Stabilizing Deep Tomographic Reconstruction

no code implementations4 Aug 2020 Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shao-Yu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang

ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.

Adversarial Attack Computed Tomography (CT) +1

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction

no code implementations8 Jul 2020 Chuang Niu, Wenxiang Cong, Fenglei Fan, Hongming Shan, Mengzhou Li, Jimin Liang, Ge Wang

Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training.

Disentanglement Metal Artifact Reduction

X-ray Photon-Counting Data Correction through Deep Learning

no code implementations6 Jul 2020 Mengzhou Li, David S. Rundle, Ge Wang

The simulated PCD data and the ground truth counterparts are then fed to a specially designed deep adversarial network for PCD data correction.

Metal Artifact Reduction

Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network

no code implementations23 Jun 2020 Qing Lyu, Hongming Shan, Yibin Xie, Debiao Li, Ge Wang

As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort.

Computed Tomography (CT) Frame +3

GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering

1 code implementation ECCV 2020 Chuang Niu, Jun Zhang, Ge Wang, Jimin Liang

To train the GATCluster in a completely unsupervised manner, we design four self-learning tasks with the constraints of transformation invariance, separability maximization, entropy analysis, and attention mapping.

Image Clustering Representation Learning +1

Quasi-Equivalence of Width and Depth of Neural Networks

no code implementations6 Feb 2020 Feng-Lei Fan, Rongjie Lai, Ge Wang

While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of deep networks.

Classification General Classification

On Interpretability of Artificial Neural Networks: A Survey

1 code implementation8 Jan 2020 Fenglei Fan, JinJun Xiong, Mengzhou Li, Ge Wang

Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success in many important areas that deal with text, images, videos, graphs, and so on.

Medical Diagnosis

Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction

1 code implementation9 Dec 2019 Huidong Xie, Hongming Shan, Wenxiang Cong, Chi Liu, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences.

Image Reconstruction

Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data

no code implementations13 Nov 2019 Huidong Xie, Hongming Shan, Ge Wang

Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture.

Computed Tomography (CT) Image Reconstruction

Multilingual Grammar Induction with Continuous Language Identification

no code implementations IJCNLP 2019 Wenjuan Han, Ge Wang, Yong Jiang, Kewei Tu

The key to multilingual grammar induction is to couple grammar parameters of different languages together by exploiting the similarity between languages.

Language Identification

Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising

1 code implementation13 Oct 2019 Yu Gong, Hongming Shan, Yueyang Teng, Ning Tu, Ming Li, Guodong Liang, Ge Wang, Shan-Shan Wang

The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN.

Image Denoising Transfer Learning

Deep-learning-based Breast CT for Radiation Dose Reduction

no code implementations25 Sep 2019 Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images.

Computed Tomography (CT) Image Reconstruction

Multi-Contrast Super-Resolution MRI Through a Progressive Network

no code implementations5 Aug 2019 Qing Lyu, Hongming Shan, Ge Wang

Our experimental results demonstrate that the proposed networks can produce MRI super-resolution images with good image quality and outperform other multi-contrast super-resolution methods in terms of structural similarity and peak signal-to-noise ratio.

Computed Tomography (CT) Image Super-Resolution

Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

no code implementations23 Jul 2019 Hongming Shan, Christopher Wiedeman, Ge Wang, Yang Yang

Photoacoustic tomography seeks to reconstruct an acoustic initial pressure distribution from the measurement of the ultrasound waveforms.

MRI Super-Resolution with Ensemble Learning and Complementary Priors

no code implementations6 Jul 2019 Qing Lyu, Hongming Shan, Ge Wang

Finally, a convolutional neural network is used for ensemble learning that synergizes the outputs of GANs into the final MR super-resolution images.

Ensemble Learning Image Super-Resolution

Knowledge-based Analysis for Mortality Prediction from CT Images

1 code implementation20 Feb 2019 Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan

Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.

Lung Cancer Diagnosis Mortality Prediction

Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising

1 code implementation17 Jan 2019 Fenglei Fan, Hongming Shan, Mannudeep K. Kalra, Ramandeep Singh, Guhan Qian, Matthew Getzin, Yueyang Teng, Juergen Hahn, Ge Wang

Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron.

Image Denoising

Soft Autoencoder and Its Wavelet Adaptation Interpretation

no code implementations31 Dec 2018 Fenglei Fan, Mengzhou Li, Yueyang Teng, Ge Wang

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields.

Deblurring Denoising

Low-Dose CT via Deep CNN with Skip Connection and Network in Network

no code implementations26 Nov 2018 Chenyu You, Linfeng Yang, Yi Zhang, Ge Wang

The use of deep convolutional (Conv) neural networks for noise reduction in Low-Dose CT (LDCT) images has recently shown a great potential in this important application.

Computed Tomography (CT)

On a Sparse Shortcut Topology of Artificial Neural Networks

1 code implementation22 Nov 2018 Fenglei Fan, Dayang Wang, Hengtao Guo, Qikui Zhu, Pingkun Yan, Ge Wang, Hengyong Yu

In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.

Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?

1 code implementation8 Nov 2018 Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K. Kalra, Ge Wang

Here we design a novel neural network architecture for low-dose CT (LDCT) and compare it with commercial iterative reconstruction methods used for standard of care CT.

Denoising Image Reconstruction

Visual Attention Network for Low Dose CT

no code implementations31 Oct 2018 Wenchao Du, Hu Chen, Peixi Liao, Hongyu Yang, Ge Wang, Yi Zhang

Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance.

Image Restoration

Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping

no code implementations30 Oct 2018 Yiming Lei, Yukun Tian, Hongming Shan, Junping Zhang, Ge Wang, Mannudeep Kalra

Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN.

Data Augmentation General Classification +2

Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images

no code implementations19 Oct 2018 Pingkun Yan, Hengtao Guo, Ge Wang, Ruben De Man, Mannudeep K. Kalra

In this paper, we propose a deep learning based method, which takes both chest LDCT image patches and coronary artery calcification risk scores as input, for direct prediction of mortality risk of lung cancer subjects.

Lung Cancer Diagnosis Mortality Prediction

Super-resolution MRI through Deep Learning

no code implementations16 Oct 2018 Qing Lyu, Chenyu You, Hongming Shan, Ge Wang

Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.

Medical Physics

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

no code implementations10 Aug 2018 Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, Michael W. Vannier, Punam K. Saha, Ge Wang

Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.

Computed Tomography (CT) Image Restoration +1

Universal Approximation with Quadratic Deep Networks

no code implementations31 Jul 2018 Fenglei Fan, JinJun Xiong, Ge Wang

(4) To approximate the same class of functions with the same error bound, is a quantized quadratic network able to enjoy a lower number of weights than a quantized conventional network?

Speech Recognition

Fuzzy Logic Interpretation of Quadratic Networks

no code implementations4 Jul 2018 Fenglei Fan, Ge Wang

Since traditional neural networks and second-order counterparts can represent each other and fuzzy logic operations are naturally implemented in second-order neural networks, it is plausible to explain how a deep neural network works with a second-order network as the system model.

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

no code implementations2 May 2018 Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang

However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.

Computed Tomography (CT) Denoising

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

no code implementations15 Feb 2018 Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang

Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch.

Computed Tomography (CT) Denoising +1

Shamap: Shape-based Manifold Learning

no code implementations15 Feb 2018 Fenglei Fan, Ziyu Su, Yueyang Teng, Ge Wang

For manifold learning, it is assumed that high-dimensional sample/data points are embedded on a low-dimensional manifold.

Dimensionality Reduction

Learning from Pseudo-Randomness With an Artificial Neural Network - Does God Play Pseudo-Dice?

no code implementations5 Jan 2018 Fenglei Fan, Ge Wang

Inspired by the fact that the neural network, as the mainstream for machine learning, has brought successes in many application areas, here we propose to use this approach for decoding hidden correlation among pseudo-random data and predicting events accordingly.

General Backpropagation Algorithm for Training Second-order Neural Networks

no code implementations17 Aug 2017 Fenglei Fan, Wenxiang Cong, Ge Wang

The artificial neural network is a popular framework in machine learning.

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

9 code implementations3 Aug 2017 Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K. Kalra, Ge Wang

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.

Image Denoising

LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT

no code implementations30 Jul 2017 Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Weihua Zhang, Huaiqiaing Sun, Yang Lv, Peixi Liao, Jiliu Zhou, Ge Wang

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.

Compressive Sensing

A New Type of Neurons for Machine Learning

no code implementations26 Apr 2017 Fenglei Fan, Wenxiang Cong, Ge Wang

Here we investigate the possibility of replacing the inner product with a quadratic function of the input vector, thereby upgrading the 1st order neuron to the 2nd order neuron, empowering individual neurons, and facilitating the optimization of neural networks.

CT Image Reconstruction in a Low Dimensional Manifold

no code implementations16 Apr 2017 Wenxiang Cong, Ge Wang, Qingsong Yang, Jiang Hsieh, Jia Li, Rongjie Lai

In this paper, we propose a CT image reconstruction method based on the prior knowledge of the low-dimensional manifold of CT image.

Image Reconstruction

CT Image Denoising with Perceptive Deep Neural Networks

no code implementations22 Feb 2017 Qingsong Yang, Pingkun Yan, Mannudeep K. Kalra, Ge Wang

Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence.

Image Denoising

Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

1 code implementation1 Feb 2017 Hu Chen, Yi Zhang, Mannudeep K. Kalra, Feng Lin, Yang Chen, Peixi Liao, Jiliu Zhou, Ge Wang

Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field.

Lesion Detection

Deep Learning for the Classification of Lung Nodules

no code implementations21 Nov 2016 He Yang, Hengyong Yu, Ge Wang

Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging.

Classification General Classification +1

Low-dose CT denoising with convolutional neural network

no code implementations2 Oct 2016 Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang

To reduce the potential radiation risk, low-dose CT has attracted much attention.

Denoising

Low-Dose CT via Deep Neural Network

no code implementations27 Sep 2016 Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang

In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention.

Medical Physics

A Perspective on Deep Imaging

no code implementations10 Sep 2016 Ge Wang

The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction.

Image Reconstruction

Recognition of convolutional neural network based on CUDA Technology

no code implementations30 May 2015 Yi-bin Huang, Kang Li, Ge Wang, Min Cao, Pin Li, Yu-jia Zhang

For the problem whether Graphic Processing Unit(GPU), the stream processor with high performance of floating-point computing is applicable to neural networks, this paper proposes the parallel recognition algorithm of Convolutional Neural Networks(CNNs). It adopts Compute Unified Device Architecture(CUDA)technology, definite the parallel data structures, and describes the mapping mechanism for computing tasks on CUDA.

Dictionary-Learning-Based Reconstruction Method for Electron Tomography

no code implementations22 Nov 2013 Baodong Liu, Hengyong Yu, Scott S. Verbridge, Lizhi Sun, Ge Wang

In this paper, we evaluate the EST, ADSIR and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes.

Compressive Sensing Dictionary Learning +1

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