no code implementations • 24 Aug 2023 • Qing Lyu, Josh Tan, Megan E. Lipford, Chuang Niu, Micheal E. Zapadka, Christopher M. Lack, Jonathan D. Clemente, Christopher T. Whitlow, Ge Wang
Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography.
no code implementations • 27 Jul 2023 • Razi Mahmood, Ge Wang, Mannudeep Kalra, Pingkun Yan
Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.
no code implementations • 18 Jul 2023 • Huidong Xie, Bo Zhou, Xiongchao Chen, Xueqi Guo, Stephanie Thorn, Yi-Hwa Liu, Ge Wang, Albert Sinusas, Chi Liu
Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer.
no code implementations • 13 Jun 2023 • Rongjun Ge, Yuting He, Cong Xia, Yang Chen, Daoqiang Zhang, Ge Wang
Multiphase contrast-enhanced computed tomography (CECT) scan is clinically significant to demonstrate the anatomy at different phases.
1 code implementation • 5 Jun 2023 • Chi Ding, Qingchao Zhang, Ge Wang, Xiaojing Ye, YunMei Chen
We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain sparse-view CT image reconstruction.
no code implementations • 3 Apr 2023 • Chuang Niu, Ge Wang
In this paper, we report a feasibility study of building a multi-task CT large image-text (LIT) model for lung cancer diagnosis by combining an LLM and a large image model (LIM).
no code implementations • 22 Mar 2023 • Wenjun Xia, Chuang Niu, Wenxiang Cong, Ge Wang
Incorporating sinogram data and performing dual-domain reconstruction improve image quality with artifact suppression, but a straightforward 3D implementation requires storing an entire 3D sinogram in memory and many parameters of dual-domain networks.
no code implementations • 19 Mar 2023 • Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li
In this work, we introduce a novel unsupervised protein structure representation pretraining with a robust protein language model.
no code implementations • 16 Mar 2023 • Qing Lyu, Josh Tan, Michael E. Zapadka, Janardhana Ponnatapura, Chuang Niu, Kyle J. Myers, Ge Wang, Christopher T. Whitlow
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities.
1 code implementation • CVPR 2023 • Jiangbin Zheng, Yile Wang, Cheng Tan, Siyuan Li, Ge Wang, Jun Xia, Yidong Chen, Stan Z. Li
In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.
no code implementations • 21 Feb 2023 • Zhihao Chen, Chuang Niu, Ge Wang, Hongming Shan
Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks.
1 code implementation • 8 Feb 2023 • Yuhui Ruan, Qiao Yuan, Chuang Niu, Chen Li, YuDong Yao, Ge Wang, Yueyang Teng
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk.
no code implementations • 7 Feb 2023 • Soham Bhosale, Arjun Krishna, Ge Wang, Klaus Mueller
The augmented dataset is then used to train a U-Net segmentation network which displays a significant improvement in the segmentation accuracy in segmenting the large non-annotated dataset.
no code implementations • 5 Feb 2023 • Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang, Stan Z. Li
Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design.
no code implementations • 20 Jan 2023 • Md Selim, Jie Zhang, Michael A. Brooks, Ge Wang, Jin Chen
This work addresses the issue of CT image harmonization using a new diffusion-based model, named DiffusionCT, to standardize CT images acquired from different vendors and protocols.
no code implementations • 18 Nov 2022 • Wenjun Xia, Wenxiang Cong, Ge Wang
A DDPM network based on patches extracted from fully sampled projection data is trained and then used to inpaint down-sampled projection data.
1 code implementation • ACL 2022 • Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Z. 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.
no code implementations • 29 Sep 2022 • Wenjun Xia, Qing Lyu, Ge Wang
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades.
no code implementations • 24 Sep 2022 • Qing Lyu, Ge Wang
To address this challenge, computational conversion is a viable approach between MRI and CT images, especially from MRI to CT images.
1 code implementation • 3 Sep 2022 • Guangtong Yang, Chen Li, YuDong Yao, Ge Wang, Yueyang Teng
In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches.
no code implementations • 26 Aug 2022 • Zihan Liu, Ge Wang, Yun Luo, Stan Z. Li
To address this issue, we propose a novel surrogate model with multi-level propagation that preserves the node dissimilarity information.
1 code implementation • 24 Jul 2022 • Zilong Li, Qi Gao, Yaping Wu, Chuang Niu, Junping Zhang, Meiyun Wang, Ge Wang, Hongming Shan
Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties.
1 code implementation • 19 Jul 2022 • Christopher Wiedeman, Ge Wang
Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed.
no code implementations • 15 Jul 2022 • Chuang Niu, Ge Wang
Minimum redundancy among different elements of an embedding in a latent space is a fundamental requirement or major preference in representation learning to capture intrinsic informational structures.
2 code implementations • 7 Jul 2022 • Zelin Zang, Siyuan Li, Di wu, Ge Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li
To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness.
Ranked #2 on
Image Classification
on ImageNet-100
no code implementations • 24 Jun 2022 • Huidong Xie, Zhao Liu, Luyao Shi, Kathleen Greco, Xiongchao Chen, Bo Zhou, Attila Feher, John C. Stendahl, Nabil Boutagy, Tassos C. Kyriakides, Ge Wang, Albert J. Sinusas, Chi Liu
In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation.
no code implementations • 15 Jun 2022 • Ge Wang, Li Tan, Tianbao Song, Wei Wang, Ziliang Shang
Specifically, a novel region-enhanced deep graph convolutional network (RDGCN) that enhances the propagation features of rumors by learning regionalized propagation patterns and trains to learn the propagation patterns by unsupervised learning is proposed.
no code implementations • 13 Jun 2022 • Chuang Niu, Ge Wang
Self-supervised representation learning maps high-dimensional data into a meaningful embedding space, where samples of similar semantic contents are close to each other.
no code implementations • 30 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.
no code implementations • 25 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.
no code implementations • 29 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.
no code implementations • 24 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.
no code implementations • 22 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.
1 code implementation • 21 Mar 2022 • Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li
This also leads to an interesting objective design problem for mixup training that we need to focus on both smoothing the decision boundaries and identifying discriminative features.
1 code implementation • 20 Jan 2022 • Qing Lyu, Christopher T. Whitlow, Ge Wang
Recently, deep learning has achieved remarkable successes in medical image analysis.
no code implementations • 23 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.
no code implementations • 30 Nov 2021 • Chuang Niu, Ge Wang
X-ray imaging is the most popular medical imaging technology.
no code implementations • 16 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.
1 code implementation • 12 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.
1 code implementation • 12 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.
1 code implementation • 7 Oct 2021 • Qing Lyu, Sanjeev V. Namjoshi, Emory McTyre, Umit Topaloglu, Richard Barcus, Michael D. Chan, Christina K. Cramer, Waldemar Debinski, Metin N. Gurcan, Glenn J. Lesser, Hui-Kuan Lin, Reginald F. Munden, Boris C. Pasche, Kiran Kumar Solingapuram Sai, Roy E. Strowd, Stephen B. Tatter, Kounosuke Watabe, Wei zhang, Ge Wang, Christopher T. Whitlow
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology.
1 code implementation • 5 Oct 2021 • Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z. Li
Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive samples) and pushes the embeddings of other samples (negatives) apart.
no code implementations • 27 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.
1 code implementation • 27 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.
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.
1 code implementation • 9 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.
no code implementations • 17 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.
no code implementations • 29 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
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
1 code implementation • 25 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.
no code implementations • 18 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.
1 code implementation • 17 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.
Ranked #1 on
Image Clustering
on CIFAR-100
no code implementations • 18 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.
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.
1 code implementation • 6 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.
no code implementations • 28 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.
2 code implementations • 16 Aug 2020 • Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population.
no code implementations • 4 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.
1 code implementation • 20 Jul 2020 • Hanqing Chao, Xi Fang, Jiajin Zhang, Fatemeh Homayounieh, Chiara D. Arru, Subba R. Digumarthy, Rosa Babaei, Hadi K. Mobin, Iman Mohseni, Luca Saba, Alessandro Carriero, Zeno Falaschi, Alessio Pasche, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally.
no code implementations • 8 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.
no code implementations • 6 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.
no code implementations • 23 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.
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.
no code implementations • 6 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.
1 code implementation • 8 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.
1 code implementation • 9 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.
no code implementations • 13 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.
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.
1 code implementation • 13 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.
no code implementations • 25 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.
no code implementations • 5 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.
no code implementations • 23 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.
no code implementations • 6 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.
no code implementations • 2 Jul 2019 • Huidong Xie, Hongming Shan, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang
Few-view CT image reconstruction is an important topic to reduce the radiation dose.
1 code implementation • 20 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.
1 code implementation • 17 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.
no code implementations • 31 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.
no code implementations • 26 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.
1 code implementation • 22 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.
1 code implementation • 8 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.
no code implementations • 31 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.
no code implementations • 30 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.
no code implementations • 19 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.
no code implementations • 16 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
no code implementations • 10 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.
no code implementations • 31 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?
no code implementations • 4 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.
no code implementations • 2 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.
no code implementations • 15 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.
no code implementations • 15 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.
no code implementations • 5 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.
no code implementations • 17 Aug 2017 • Fenglei Fan, Wenxiang Cong, Ge Wang
The artificial neural network is a popular framework in machine learning.
9 code implementations • 3 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.
no code implementations • 30 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.
no code implementations • 26 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.
no code implementations • 16 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.
no code implementations • 22 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.
1 code implementation • 1 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.
no code implementations • 21 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.
no code implementations • 2 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.
no code implementations • 27 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
no code implementations • 10 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.
no code implementations • 30 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.
no code implementations • 22 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.