1,199 results match your criteria generative adversarial

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Artif Intell Med 2021 Jul 14;117:102109. Epub 2021 May 14.

IMT Atlantique, Technopôle Brest-Iroise, 29238 Brest, France; LaTIM UMR 1101, Inserm, 22 avenue Camille Desmoulins, 29238 Brest, France.

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks. Read More

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Multi-modal MRI Image Synthesis via GAN with Multi-scale Gate Mergence.

IEEE J Biomed Health Inform 2021 Jun 14;PP. Epub 2021 Jun 14.

Multi-modal magnetic resonance imaging (MRI) plays a critical role in clinical diagnosis and treatment nowadays. Each modality of MRI presents its own specific anatomical features which serve as complementary information to other modalities and can provide rich diagnostic information. However, due to the limitations of time consuming and expensive cost, some image sequences of patients may be lost or corrupted, posing an obstacle for accurate diagnosis. Read More

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Learning across Tasks for Zero-Shot Domain Adaptation from a Single Source Domain.

IEEE Trans Pattern Anal Mach Intell 2021 Jun 14;PP. Epub 2021 Jun 14.

Human beings are experts in generalization across domains. For example, a baby can easily identify the bear from a clipart image after learning this category of animal from the photo images. To reduce the gap between the generalization ability of human and that of machines, we propose a new solution to the challenging zero-shot domain adaptation (ZSDA) problem, where only a single source domain is available and the target domain for the task of interest is unseen. Read More

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COVID-19 Diagnosis on CT Scan Images Using a Generative Adversarial Network and Concatenated Feature Pyramid Network with an Attention Mechanism.

Med Phys 2021 Jun 12. Epub 2021 Jun 12.

School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, 650500, China.

Objective: Coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of infections and deaths. Efficient diagnostic methods could help curb its global spread. The purpose of this study was to develop and evaluate a method for accurately diagnosing COVID-19 based on computed tomography (CT) scans in real time. Read More

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Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models.

Magn Reson Imaging 2021 Jun 8;81:60-66. Epub 2021 Jun 8.

Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Gloriastrasse 35, 8092 Zürich, Switzerland; AI Medial AG, Zollikon, Switzerland. Electronic address:

We study two state of the art deep generative networks, the Introspective Variational Autoencoder and the Style-Based Generative Adversarial Network, for the generation of new diffusion-weighted magnetic resonance images. We show that high quality, diverse and realistic-looking images, as evaluated by external neuroradiologists blinded to the whole study, can be synthesized using these deep generative models. We evaluate diverse metrics with respect to quality and diversity of the generated synthetic brain images. Read More

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Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging.

SoftwareX 2019 Jul-Dec;10. Epub 2019 Oct 29.

Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America.

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Read More

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October 2019

Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization.

IEEE Trans Neural Netw Learn Syst 2021 Jun 10;PP. Epub 2021 Jun 10.

Image clustering has recently attracted significant attention due to the increased availability of unlabeled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tackling either unprocessed images or high-dimensional features extracted from processed images. Read More

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Generative Dual-Adversarial Network With Spectral Fidelity and Spatial Enhancement for Hyperspectral Pansharpening.

IEEE Trans Neural Netw Learn Syst 2021 Jun 10;PP. Epub 2021 Jun 10.

Hyperspectral (HS) pansharpening is of great importance in improving the spatial resolution of HS images for remote sensing tasks. HS image comprises abundant spectral contents, whereas panchromatic (PAN) image provides spatial information. HS pansharpening constitutes the possibility for providing the pansharpened image with both high spatial and spectral resolution. Read More

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Zero-Shot Learning via Structure-Aligned Generative Adversarial Network.

IEEE Trans Neural Netw Learn Syst 2021 Jun 9;PP. Epub 2021 Jun 9.

In this article, we propose a structure-aligned generative adversarial network framework to improve zero-shot learning (ZSL) by mitigating the semantic gap, domain shift, and hubness problem. The proposed framework contains two parts, i.e. Read More

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Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs.

SN Comput Sci 2021 4;2(4):321. Epub 2021 Jun 4.

School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, 1 Jan Smuts Avenue, Braamfontein South Africa.

Chest X-rays are a vital diagnostic tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled images to drive forward the development of diagnostic tools; however, this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals. Read More

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Using Neural Networks to Extend Cropped Medical Images for Deformable Registration Among Images with Differing Scan Extents.

Med Phys 2021 Jun 8. Epub 2021 Jun 8.

Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, 90024, USA.

Purpose: Missing or discrepant imaging volumes is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.

Methods: Using a training dataset of 409 head and neck CT's, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. Read More

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Synthesizing PET/MR (T1-weighted) images from non-attenuation-corrected PET images.

Phys Med Biol 2021 Jun 7. Epub 2021 Jun 7.

Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, P.R.China, Shenzhen, 518055, CHINA.

Positron emission tomography (PET) imaging can be used for early detection, diagnosis and postoperative patient monitoring of many diseases. Traditional PET imaging requires not only additional computed tomography (CT) imaging or magnetic resonance imaging (MR) to provide anatomical information but also attenuation correction (AC) map calculation based on CT images or MR images for accurate quantitative estimation. During a patient's treatment, PET/CT or PET/MR scans are inevitably repeated many times, leading to additional doses of ionizing radiation (CT scans) and additional economic and time costs (MR scans). Read More

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Domain Shift Preservation for Zero-Shot Domain Adaptation.

IEEE Trans Image Process 2021 11;30:5505-5517. Epub 2021 Jun 11.

In learning-based image processing a model that is learned in one domain often performs poorly in another since the image samples originate from different sources and thus have different distributions. Domain adaptation techniques alleviate the problem of domain shift by learning transferable knowledge from the source domain to the target domain. Zero-shot domain adaptation (ZSDA) refers to a category of challenging tasks in which no target-domain sample for the task of interest is accessible for training. Read More

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Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Synthetic CT Generation.

2020 IEEE 21st Int Conf Inf Reuse Integr Data Sci (2020) 2020 Aug 10;2020:188-193. Epub 2020 Sep 10.

Henry Ford Health System, Department of Radiation Oncology, Detroit, MI 48202, USA.

Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that contain atypical anatomy remains a major limitation. In this paper, we propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI images as the input to address atypical anatomy. Read More

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Geometric and dosimetric impact of 3D generative adversarial network-based metal artifact reduction algorithm on VMAT and IMPT for the head and neck region.

Radiat Oncol 2021 Jun 6;16(1):96. Epub 2021 Jun 6.

Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.

Background: We investigated the geometric and dosimetric impact of three-dimensional (3D) generative adversarial network (GAN)-based metal artifact reduction (MAR) algorithms on volumetric-modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT) for the head and neck region, based on artifact-free computed tomography (CT) volumes with dental fillings.

Methods: Thirteen metal-free CT volumes of the head and neck regions were obtained from The Cancer Imaging Archive. To simulate metal artifacts on CT volumes, we defined 3D regions of the teeth for pseudo-dental fillings from the metal-free CT volumes. Read More

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The energy distance for ensemble and scenario reduction.

Florian Ziel

Philos Trans A Math Phys Eng Sci 2021 Jul 7;379(2202):20190431. Epub 2021 Jun 7.

House of Energy Markets and Finance, University of Duisburg-Essen, Essen, Nordrhein-Westfalen, Germany.

Scenario reduction techniques are widely applied for solving sophisticated dynamic and stochastic programs, especially in energy and power systems, but are also used in probabilistic forecasting, clustering and estimating generative adversarial networks. We propose a new method for ensemble and scenario reduction based on the energy distance which is a special case of the maximum mean discrepancy. We discuss the choice of energy distance in detail, especially in comparison to the popular Wasserstein distance which is dominating the scenario reduction literature. Read More

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Pixel-Wise Wasserstein Autoencoder for Highly Generative Dehazing.

IEEE Trans Image Process 2021 9;30:5452-5462. Epub 2021 Jun 9.

We propose a highly generative dehazing method based on pixel-wise Wasserstein autoencoders. In contrast to existing dehazing methods based on generative adversarial networks, our method can produce a variety of dehazed images with different styles. It significantly improves the dehazing accuracy via pixel-wise matching from hazy to dehazed images through 2-dimensional latent tensors of the Wasserstein autoencoder. Read More

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Age-Oriented Face Synthesis With Conditional Discriminator Pool and Adversarial Triplet Loss.

IEEE Trans Image Process 2021 7;30:5413-5425. Epub 2021 Jun 7.

The vanilla Generative Adversarial Networks (GANs) are commonly used to generate realistic images depicting aged and rejuvenated faces. However, the performance of such vanilla GANs in the age-oriented face synthesis task is often compromised by the mode collapse issue, which may produce poorly synthesized faces with indistinguishable visual variations. In addition, recent age-oriented face synthesis methods use the L1 or L2 constraint to preserve the identity information in synthesized faces, which implicitly limits the identity permanence capabilities when these constraints are associated with a trivial weighting factor. Read More

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A Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Symptom Auditor.

SN Comput Sci 2021 28;2(4):304. Epub 2021 May 28.

Department of Computer Science and Engineering, SVCET, Chittoor, Andhra Pradesh India.

In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. Read More

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SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks.

Sensors (Basel) 2021 May 24;21(11). Epub 2021 May 24.

Department of Electrical & Computer Engineering, National University of Singapore, Singapore 117576, Singapore.

Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network's ability to extract blurred image features. Read More

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A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data.

Sensors (Basel) 2021 May 27;21(11). Epub 2021 May 27.

Consiglio Nazionale delle Ricerche (CNR), Institute of Electronics, Information Engineering and Telecommunications (IEIIT), 16149 Genoa, Italy.

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the "Internet of Medical Things" (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. Read More

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Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach.

Diagnostics (Basel) 2021 May 18;11(5). Epub 2021 May 18.

Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.

COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Read More

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RRG-GAN Restoring Network for Simple Lens Imaging System.

Sensors (Basel) 2021 May 11;21(10). Epub 2021 May 11.

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

The simple lens computational imaging method provides an alternative way to achieve high-quality photography. It simplifies the design of the optical-front-end to a single-convex-lens and delivers the correction of optical aberration to a dedicated computational restoring algorithm. Traditional single-convex-lens image restoration is based on optimization theory, which has some shortcomings in efficiency and efficacy. Read More

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A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty.

Sensors (Basel) 2021 May 21;21(11). Epub 2021 May 21.

School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea.

Owing to the increasing use of permeable pavement, there is a growing need for studies that can improve its design and durability. One of the most important factors that can reduce the functionality of permeable pavement is the clogging issue. Field experiments for investigating the clogging potential are relatively expensive owing to the high-cost testing equipment and materials. Read More

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Unsupervised Exemplar-Domain Aware Image-to-Image Translation.

Entropy (Basel) 2021 May 2;23(5). Epub 2021 May 2.

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

Image-to-image translation is used to convert an image of a certain style to another of the target style with the original content preserved. A desired translator should be capable of generating diverse results in a controllable many-to-many fashion. To this end, we design a novel deep translator, namely exemplar-domain aware image-to-image translator (EDIT for short). Read More

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Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction.

Comput Biol Med 2021 May 26;134:104504. Epub 2021 May 26.

Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK. Electronic address:

Deep learning based generative adversarial networks (GAN) can effectively perform image reconstruction with under-sampled MR data. In general, a large number of training samples are required to improve the reconstruction performance of a certain model. However, in real clinical applications, it is difficult to obtain tens of thousands of raw patient data to train the model since saving k-space data is not in the routine clinical flow. Read More

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An image-computable model of human visual shape similarity.

PLoS Comput Biol 2021 Jun 1;17(6):e1008981. Epub 2021 Jun 1.

Department of Experimental Psychology, Justus-Liebig University Giessen, Giessen, Germany.

Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. Read More

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Temporal and spectral unmixing of photoacoustic signals by deep learning.

Opt Lett 2021 Jun;46(11):2690-2693

Improving the imaging speed of multi-parametric photoacoustic microscopy (PAM) is essential to leveraging its impact in biomedicine. However, to avoid temporal overlap, the A-line rate is limited by the acoustic speed in biological tissues to a few megahertz. Moreover, to achieve high-speed PAM of the oxygen saturation of hemoglobin, the stimulated Raman scattering effect in optical fibers has been widely used to generate 558 nm from a commercial 532 nm laser for dual-wavelength excitation. Read More

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A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.

Phys Med Biol 2021 May 31;66(11). Epub 2021 May 31.

School of Medicine, University of Maryland, Baltimore, MD, United States of America.

. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.. Read More

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Weakly Supervised Discriminative Learning With Spectral Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection.

IEEE Trans Neural Netw Learn Syst 2021 May 31;PP. Epub 2021 May 31.

Anomaly detection (AD) using hyperspectral images (HSIs) is of great interest for deep space exploration and Earth observations. This article proposes a weakly supervised discriminative learning with a spectral constrained generative adversarial network (GAN) for hyperspectral anomaly detection (HAD), called weaklyAD. It can enhance the discrimination between anomaly and background with background homogenization and anomaly saliency in cases where anomalous samples are limited and sensitive to the background. Read More

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