Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma
Sign Up to like & getrecommendations! Published in 2022 at "JAMA Ophthalmology"
DOI: 10.1001/jamaophthalmol.2022.3375
Abstract: This cross-sectional study assesses whether generative models can synthesize circumpapillary optic nerve head optical coherence tomography (OCT) images of normal and glaucomatous eyes and determines the usability of synthetic images for training deep learning models… read more here.
Keywords: coherence tomography; generative adversarial; optical coherence; evaluation generative ... See more keywords
Lung cancer classification using exponential mean saturation linear unit activation function in various generative adversarial network models
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22719
Abstract: Nowadays, the mortality rate due to lung cancer increases rapidly worldwide as it can be classified only at the later stages. Early classification of lung cancer will help patients to take treatment and decrease the… read more here.
Keywords: network; adversarial network; classification; generative adversarial ... See more keywords
Generating synthetic CTs from magnetic resonance images using generative adversarial networks
Sign Up to like & getrecommendations! Published in 2018 at "Medical Physics"
DOI: 10.1002/mp.13047
Abstract: PURPOSE While MR-only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real-time MR-only planning. This… read more here.
Keywords: generative adversarial; adversarial networks; model; synthetic cts ... See more keywords
Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.
Sign Up to like & getrecommendations! Published in 2020 at "Medical physics"
DOI: 10.1002/mp.14075
Abstract: PURPOSE The purpose of this study is to investigate the effect of different MR sequences on the accuracy of deep learning based synthetic CT (sCT) generation in the complex head and neck region. METHODS Four… read more here.
Keywords: generative adversarial; image; based synthetic; network ... See more keywords
Phase Identification for Dynamic CT enhancements with Generative Adversarial Network.
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DOI: 10.1002/mp.14706
Abstract: PURPOSE Dynamic contrast enhanced computed tomography (CT) is widely used to provide dynamic tissue contrast for diagnostic investigation and vascular identification. However, the phase information of contrast injection is typically recorded manually by technicians, which… read more here.
Keywords: generative adversarial; identification dynamic; network; contrast ... See more keywords
Clinical ultra high resolution CT scans enabled by using a generative adversarial network.
Sign Up to like & getrecommendations! Published in 2022 at "Medical physics"
DOI: 10.1002/mp.16172
Abstract: BACKGROUND Ultra-high resolution computed tomography (UHRCT) has shown great potential for the detection of pulmonary diseases. However, UHRCT scanning generally induces increases in scanning time and radiation exposure. Super resolution is a gradually prosperous application… read more here.
Keywords: ultra high; adversarial network; high resolution; generative adversarial ... See more keywords
Photo-realistic dehazing via contextual generative adversarial networks
Sign Up to like & getrecommendations! Published in 2020 at "Machine Vision and Applications"
DOI: 10.1007/s00138-020-01082-5
Abstract: Single image dehazing is a challenging task due to its ambiguous nature. In this paper we present a new model based on generative adversarial networks (GANs) for single image dehazing, called as dehazing GAN. In… read more here.
Keywords: generative adversarial; dehazing via; image; realistic dehazing ... See more keywords
EMOCGAN: a novel evolutionary multiobjective cyclic generative adversarial network and its application to unpaired image translation
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-05975-y
Abstract: Generative adversarial networks (GANs) have been accepted as powerful models in the field of computer vision, speech and language processing, etc. However, a major concern regarding GANs is the requirement of paired images for image-to-image… read more here.
Keywords: generative adversarial; novel evolutionary; image; cyclic gan ... See more keywords
GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)
Sign Up to like & getrecommendations! Published in 2021 at "Mathematical Geosciences"
DOI: 10.1007/s11004-021-09934-0
Abstract: Conditional facies modeling combines geological spatial patterns with different types of observed data, to build earth models for predictions of subsurface resources. Recently, researchers have used generative adversarial networks (GANs) for conditional facies modeling, where… read more here.
Keywords: generative adversarial; adversarial networks; networks gans; conditional facies ... See more keywords
Font generation based on least squares conditional generative adversarial nets
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-017-5457-4
Abstract: With the rapid growth of multimedia information, the font library has become a part of people’s work life. Compared to the Western alphabet language, it is difficult to create new font due to huge quantity… read more here.
Keywords: generative adversarial; adversarial nets; font generation; based least ... See more keywords
Image enhancement algorithm based on generative adversarial network in combination of improved game adversarial loss mechanism
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-07776-x
Abstract: The feed-forward architectures of recently proposed generative adversarial network can learn the non-linear mapping from low-resolution output to high-resolution output. However, this approach does not fully address the mutual dependencies of different resolution images. By… read more here.
Keywords: generative adversarial; game; image; network ... See more keywords