The Experts below are selected from a list of 16086 Experts worldwide ranked by ideXlab platform
Ge Wang - One of the best experts on this subject based on the ideXlab platform.
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3 d convolutional encoder decoder Network for low dose ct via transfer learning from a 2 d Trained Network
IEEE Transactions on Medical Imaging, 2018Co-Authors: Hongming Shan, Mannudeep K Kalra, Uwe Krüger, Wenxiang Cong, Qingsong Yang, Yi Zhang, Ge WangAbstract:Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural Network (CNN) and generative adversarial Network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) Network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a Trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D Network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE Network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.
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3d convolutional encoder decoder Network for low dose ct via transfer learning from a 2d Trained Network
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Hongming Shan, Uwe Krüger, Wenxiang Cong, Qingsong Yang, Yi Zhang, Ge WangAbstract:Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural Network (CNN) and generative adversarial Network (GAN). This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) Network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a Trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices. 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. By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures.
Tan Zhiming - One of the best experts on this subject based on the ideXlab platform.
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CVPR Workshops - Variable Rate Image Compression Method With Dead-Zone Quantizer
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Jing Zhou, Akira Nakagawa, Keizo Kato, Sihan Wen, Kimihiko Kazui, Tan ZhimingAbstract:Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplier λ. For conventional codec, signal is decorrelated with orthonormal transformation, and uniform quantizer is introduced. We propose a variable rate image compression method with dead-zone quantizer. Firstly, the autoencoder Network is Trained with RaDOGAGA [6] framework, which can make the latents isometric to the metric space, such as SSIM and MSE. Then the conventional dead-zone quantization method with arbitrary step size is used in the common Trained Network to provide the flexible rate control. With dead-zone quantizer, the experimental results show that our method performs comparably with independently optimized models within a wide range of bitrate.
Hongming Shan - One of the best experts on this subject based on the ideXlab platform.
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3 d convolutional encoder decoder Network for low dose ct via transfer learning from a 2 d Trained Network
IEEE Transactions on Medical Imaging, 2018Co-Authors: Hongming Shan, Mannudeep K Kalra, Uwe Krüger, Wenxiang Cong, Qingsong Yang, Yi Zhang, Ge WangAbstract:Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural Network (CNN) and generative adversarial Network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) Network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a Trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D Network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE Network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.
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3d convolutional encoder decoder Network for low dose ct via transfer learning from a 2d Trained Network
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Hongming Shan, Uwe Krüger, Wenxiang Cong, Qingsong Yang, Yi Zhang, Ge WangAbstract:Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural Network (CNN) and generative adversarial Network (GAN). This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) Network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a Trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices. 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. By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures.
Jing Zhou - One of the best experts on this subject based on the ideXlab platform.
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variable rate image compression method with dead zone quantizer
arXiv: Image and Video Processing, 2020Co-Authors: Jing Zhou, Akira Nakagawa, Keizo Kato, Sihan Wen, Kimihiko Kazui, Zhiming TanAbstract:Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplier $\lambda$. For conventional codec, signal is decorrelated with orthonmal transformation, and uniform quantizer is introduced. We propose a variable rate image compression method with dead-zone quantizer. Firstly, the autoencoder Network is Trained with RaDOGAGA \cite{radogaga} framework, which can make the latents isometric to the metric space, such as SSIM and MSE. Then the conventional dead-zone quantization method with arbitrary step size is used in the common Trained Network to provide the flexible rate control. With dead-zone quantizer, the experimental results show that our method performs comparably with independently optimized models within a wide range of bitrate.
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CVPR Workshops - Variable Rate Image Compression Method With Dead-Zone Quantizer
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Jing Zhou, Akira Nakagawa, Keizo Kato, Sihan Wen, Kimihiko Kazui, Tan ZhimingAbstract:Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplier λ. For conventional codec, signal is decorrelated with orthonormal transformation, and uniform quantizer is introduced. We propose a variable rate image compression method with dead-zone quantizer. Firstly, the autoencoder Network is Trained with RaDOGAGA [6] framework, which can make the latents isometric to the metric space, such as SSIM and MSE. Then the conventional dead-zone quantization method with arbitrary step size is used in the common Trained Network to provide the flexible rate control. With dead-zone quantizer, the experimental results show that our method performs comparably with independently optimized models within a wide range of bitrate.
Shih-fu Chang - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Embedding Learning via Invariant and Spreading Instance Feature.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Xu Zhang, Pong C. Yuen, Shih-fu ChangAbstract:This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-Trained Network over samples from fine-grained categories.
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CVPR - Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Co-Authors: Xu Zhang, Pong C. Yuen, Shih-fu ChangAbstract:This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-Trained Network over samples from fine-grained categories.
