The Experts below are selected from a list of 30612 Experts worldwide ranked by ideXlab platform
Won-ki Jeong - One of the best experts on this subject based on the ideXlab platform.
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compressed sensing mri reconstruction using a generative Adversarial network with a cyclic loss
IEEE Transactions on Medical Imaging, 2018Co-Authors: Tran Minh Quan, Thanh Nguyenduc, Won-ki JeongAbstract:Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative Adversarial Model, RefineGAN , for fast and accurate CS-MRI reconstruction. The proposed Model is a variant of fully-residual convolutional autoencoder and generative Adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled $k$ -space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate—the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a $256\times 256$ image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.
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Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss
IEEE Transactions on Medical Imaging, 2018Co-Authors: Tran Minh Quan, Thanh Nguyen-duc, Won-ki JeongAbstract:Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative Adversarial Model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed Model is a variant of fully-residual convolutional autoencoder and generative Adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate -- the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256x256 image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.
Kurt Keutzer - One of the best experts on this subject based on the ideXlab platform.
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emotiongan unsupervised domain adaptation for learning discrete probability distributions of image emotions
ACM Multimedia, 2018Co-Authors: Sicheng Zhao, Guiguang Ding, Xin Zhao, Kurt KeutzerAbstract:Deep neural networks have performed well on various benchmark vision tasks with large-scale labeled training data; however, such training data is expensive and time-consuming to obtain. Due to domain shift or dataset bias, directly transferring Models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain often results in poor performance. In this paper, we consider the domain adaptation problem in image emotion recognition. Specifically, we study how to adapt the discrete probability distributions of image emotions from a source domain to a target domain in an unsupervised manner. We develop a novel Adversarial Model for emotion distribution learning, termed EmotionGAN, which alternately optimizes the Generative Adversarial Network (GAN) loss, semantic consistency loss, and regression loss. The EmotionGAN Model can adapt source domain images such that they appear as if they were drawn from the target domain, while preserving the annotation information. Extensive experiments are conducted on the FlickrLDL and TwitterLDL datasets, and the results demonstrate the superiority of the proposed method as compared to state-of-the-art approaches.
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a novel domain adaptation framework for medical image segmentation
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Amir Gholami, Sicheng Zhao, Shashank Subramanian, Varun Shenoy, Naveen Himthani, Xiangyu Yue, Peter Jin, George Biros, Kurt KeutzerAbstract:We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose an automatic method to segment white and gray matter, and cerebrospinal fluid, in addition to tumorous tissue. Regarding our first innovation, we use a domain adaptation framework that combines a novel multispecies biophysical tumor growth Model with a generative Adversarial Model to create realistic looking synthetic multimodal MR images with known segmentation. Regarding our second innovation, we propose an automatic approach to enrich available segmentation data by computing the segmentation for healthy tissues. This segmentation, which is done using diffeomorphic image registration between the BraTS training data and a set of prelabeled atlases, provides more information for training and reduces the class imbalance problem. Our overall approach is not specific to any particular neural network and can be used in conjunction with existing solutions. We demonstrate the performance improvement using a 2D U-Net for the BraTS'18 segmentation challenge. Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN Model used to create synthetic data for training.
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a novel domain adaptation framework for medical image segmentation
International MICCAI Brainlesion Workshop, 2018Co-Authors: Amir Gholami, Sicheng Zhao, Shashank Subramanian, Varun Shenoy, Naveen Himthani, Xiangyu Yue, Peter Jin, George Biros, Kurt KeutzerAbstract:We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose an automatic method to segment white matter, gray matter, glial matter and cerebrospinal fluid, in addition to tumorous tissue. Regarding our first innovation, we use a domain adaptation framework that combines a novel multispecies biophysical tumor growth Model with a generative Adversarial Model to create realistic looking synthetic multimodal MR images with known segmentation. These images are used for the purpose of training time data augmentation. Regarding our second innovation, we propose an automatic approach to enrich available segmentation data by computing the segmentation for healthy tissues. This segmentation, which is done using diffeomorphic image registration between the BraTS training data and a set of pre-labeled atlases, provides more information for training and reduces the class imbalance problem. Our overall approach is not specific to any particular neural network and can be used in conjunction with existing solutions. We demonstrate the performance improvement using a 2D U-Net for the BraTS’18 segmentation challenge. Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN Model used to create synthetic data for training.
Henry C H Chen - One of the best experts on this subject based on the ideXlab platform.
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enabling data integrity protection in regenerating coding based cloud storage theory and implementation
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Henry C H ChenAbstract:To protect outsourced data in cloud storage against corruptions, adding fault tolerance to cloud storage, along with efficient data integrity checking and recovery procedures, becomes critical. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than traditional erasure codes during failure recovery. Therefore, we study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. We design and implement a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving its intrinsic properties of fault tolerance and repair-traffic saving. Our DIP scheme is designed under a mobile Byzantine Adversarial Model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for a performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a real cloud storage testbed under different parameter choices. We further analyze the security strengths of our DIP scheme via mathematical Models. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment.
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enabling data integrity protection in regenerating coding based cloud storage
Symposium on Reliable Distributed Systems, 2012Co-Authors: Henry C H Chen, Patrick P C LeeAbstract:To protect outsourced data in cloud storage against corruptions, enabling integrity protection, fault tolerance, and efficient recovery for cloud storage becomes critical. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than traditional erasure codes during failure recovery. Therefore, we study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. We design and implement a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving the intrinsic properties of fault tolerance and repair traffic saving. Our DIP scheme is designed under a Byzantine Adversarial Model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for the performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a real cloud storage test bed under different parameter choices. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment.
Cesar Cadena - One of the best experts on this subject based on the ideXlab platform.
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empty cities image inpainting for a dynamic object invariant space
International Conference on Robotics and Automation, 2019Co-Authors: Berta Bescos, Roland Siegwart, Jose L Neira, Cesar CadenaAbstract:In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objective encounters two main challenges: detecting all the dynamic objects, and inpainting the static occluded background with plausible imagery. The former challenge is addressed by the use of a convolutional network that learns a multiclass semantic segmentation of the image. The second problem is approached with a conditional generative Adversarial Model that, taking as input the original dynamic image and its dynamic/static binary mask, is capable of generating the final static image. These generated images can be used for applications such as augmented reality or vision-based robot localization purposes. To validate our approach, we show both qualitative and quantitative comparisons against other state-of-the-art inpainting methods by removing the dynamic objects and hallucinating the static structure behind them. Furthermore, to demonstrate the potential of our results, we carry out pilot experiments that show the benefits of our proposal for visual place recognition 1.1All our code has been made available on https://github.com/bertabescos/EmptyCities.
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empty cities image inpainting for a dynamic object invariant space
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Berta Bescos, Roland Siegwart, Jose L Neira, Cesar CadenaAbstract:In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objective encounters two main challenges: detecting all the dynamic objects, and inpainting the static occluded background with plausible imagery. The second problem is approached with a conditional generative Adversarial Model that, taking as input the original dynamic image and its dynamic/static binary mask, is capable of generating the final static image. The former challenge is addressed by the use of a convolutional network that learns a multi-class semantic segmentation of the image. These generated images can be used for applications such as augmented reality or vision-based robot localization purposes. To validate our approach, we show both qualitative and quantitative comparisons against other state-of-the-art inpainting methods by removing the dynamic objects and hallucinating the static structure behind them. Furthermore, to demonstrate the potential of our results, we carry out pilot experiments that show the benefits of our proposal for visual place recognition.
Tran Minh Quan - One of the best experts on this subject based on the ideXlab platform.
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compressed sensing mri reconstruction using a generative Adversarial network with a cyclic loss
IEEE Transactions on Medical Imaging, 2018Co-Authors: Tran Minh Quan, Thanh Nguyenduc, Won-ki JeongAbstract:Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative Adversarial Model, RefineGAN , for fast and accurate CS-MRI reconstruction. The proposed Model is a variant of fully-residual convolutional autoencoder and generative Adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled $k$ -space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate—the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a $256\times 256$ image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.
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Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss
IEEE Transactions on Medical Imaging, 2018Co-Authors: Tran Minh Quan, Thanh Nguyen-duc, Won-ki JeongAbstract:Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative Adversarial Model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed Model is a variant of fully-residual convolutional autoencoder and generative Adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate -- the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256x256 image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.