37 results match your criteria gans difficult

Deep learning-based multi-modal computing with feature disentanglement for MRI image synthesis.

Med Phys 2021 May 7. Epub 2021 May 7.

School of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.

Purpose: Different Magnetic resonance imaging (MRI) modalities of the same anatomical structure are required to present different pathological information from the physical level for diagnostic needs. However, it is often difficult to obtain full-sequence MRI images of patients owing to limitations such as time consumption and high cost. The purpose of this work is to develop an algorithm for target MRI sequences prediction with high accuracy, and provide more information for clinical diagnosis. Read More

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MB-GAN: Microbiome Simulation via Generative Adversarial Network.

Gigascience 2021 Feb;10(2)

University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Population and Data Sciences, 5323 Harry Hines Blvd, Dallas, TX 75390, USA.

Background: Trillions of microbes inhabit the human body and have a profound effect on human health. The recent development of metagenome-wide association studies and other quantitative analysis methods accelerate the discovery of the associations between human microbiome and diseases. To assess the strengths and limitations of these analytical tools, simulating realistic microbiome datasets is critically important. Read More

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February 2021

Lower dimensional kernels for video discriminators.

Neural Netw 2020 Dec 26;132:506-520. Epub 2020 Sep 26.

Robust Autonomy and Decisions Group, The School of Informatics, The University of Edinburgh, 10 Crichton St, Edinburgh EH8 9AB, United Kingdom; The Edinburgh Centre of Robotics, The University of Edinburgh's Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, United Kingdom; FiveAI, 5th Floor, Greenside, 12 Blenheim Place, Edinburgh, EH7 5JH, United Kingdom.

This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimization difficult. We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases. Read More

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December 2020

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:918-922

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. Read More

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Quasi-symmetry effects in the threshold photoelectron spectrum of methyl isocyanate.

J Chem Phys 2020 Aug;153(7):074308

Université Paris-Saclay, CNRS, Institut des Sciences Moléculaires d'Orsay, 91405 Orsay, France.

The vacuum-ultraviolet threshold photoelectron spectrum of methyl isocyanate CHNCO has been recorded from 10.4 eV to 12 eV using synchrotron radiation and a coincidence technique allowing for a mass-discrimination of the photoelectron signal. A significant improvement is achieved over previous investigations as this experimental setup leads to a much more resolved spectrum. Read More

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BigGAN-based Bayesian Reconstruction of Natural Images from Human Brain Activity.

Neuroscience 2020 09 28;444:92-105. Epub 2020 Jul 28.

PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China. Electronic address:

In the visual decoding domain, the most difficult task is the visual reconstruction aimed at reconstructing the presented visual stimuli given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI), especially when reconstructing viewed natural images. Recent research regarded the visual reconstruction as the conditional image generation on fMRI voxels and started to use the generative adversarial networks (GANs) to design computational models for this task. Despite the great improvement in previous GAN-based methods, the fidelity and naturalness of the reconstructed images are still unsatisfactory, the reasons include the small number of fMRI data samples and the instability of GAN training. Read More

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September 2020

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Sci Rep 2020 07 6;10(1):11073. Epub 2020 Jul 6.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Read More

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Recurrent GANs Password Cracker For IoT Password Security Enhancement.

Sensors (Basel) 2020 May 31;20(11). Epub 2020 May 31.

Graduate School of Information Security, Korea University, Seoul 02841, Korea.

Text-based passwords are a fundamental and popular means of authentication. Password authentication can be simply implemented because it does not require any equipment, unlike biometric authentication, and it relies only on the users' memory. This reliance on memory is a weakness of passwords, and people therefore usually use easy-to-remember passwords, such as "iloveyou1234". Read More

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Generative modeling for label-free glomerular modeling and classification.

Proc SPIE Int Soc Opt Eng 2020 Feb 16;11320. Epub 2020 Mar 16.

Department of Pathology and Anatomical Sciences, SUNY Buffalo.

Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. Read More

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February 2020

Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes.

Radiol Phys Technol 2020 Jun 1;13(2):160-169. Epub 2020 May 1.

Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan.

It is often difficult to distinguish between benign and malignant pulmonary nodules using only image diagnosis. A biopsy is performed when malignancy is suspected based on CT examination. However, biopsies are highly invasive, and patients with benign nodules may undergo unnecessary procedures. Read More

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Generative adversarial networks with decoder-encoder output noises.

Neural Netw 2020 Jul 9;127:19-28. Epub 2020 Apr 9.

Department of Electrical and Electronical Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China. Electronic address:

In recent years, research on image generation has been developing very fast. The generative adversarial network (GAN) emerges as a promising framework, which uses adversarial training to improve the generative ability of its generator. However, since GAN and most of its variants use randomly sampled noises as the input of their generators, they have to learn a mapping function from a whole random distribution to the image manifold. Read More

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GAN-based synthetic brain PET image generation.

Brain Inform 2020 Mar 30;7(1). Epub 2020 Mar 30.

Department of Computer Science, Georgia State University, Atlanta, Georgia, 30302-5060, USA.

In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. Read More

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Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

PLoS One 2020 5;15(3):e0229951. Epub 2020 Mar 5.

Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, Japan.

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant features and achieved an accuracy of 81.0%. Read More

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Effects of initial microbial biomass abundance on respiration during pine litter decomposition.

PLoS One 2020 14;15(2):e0224641. Epub 2020 Feb 14.

Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.

Importance: Microbial biomass is one of the most common microbial parameters used in land carbon (C) cycle models, however, it is notoriously difficult to measure accurately. To understand the consequences of mismeasurement, as well as the broader importance of microbial biomass abundance as a direct driver of ecological phenomena, greater quantitative understanding of the role of microbial biomass abundance in environmental processes is needed. Using microcosms, we manipulated the initial biomass of numerous microbial communities across a 100-fold range and measured effects on CO2 production during plant litter decomposition. Read More

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fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.

J Neural Eng 2020 02 19;17(1):016068. Epub 2020 Feb 19.

Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Japan.

Objective: Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain-computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train classification models; hence, the fNIRS-BCI accuracy decreases.

Approach: In this study, to improve the fNIRS-BCI accuracy, we examined an fNIRS data augmentation method using Wasserstein generative adversarial networks (WGANs). Read More

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February 2020

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination.

IEEE Trans Image Process 2019 Dec 28;28(12):6225-6236. Epub 2019 Jun 28.

Though generative adversarial networks (GANs) can hallucinate high-quality high-resolution (HR) faces from low-resolution (LR) faces, they cannot ensure identity preservation during face hallucination, making the HR faces difficult to recognize. To address this problem, we propose a Siamese GAN (SiGAN) to reconstruct HR faces that visually resemble their corresponding identities. On top of a Siamese network, the proposed SiGAN consists of a pair of two identical generators and one discriminator. Read More

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December 2019

Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks.

Biomed Res Int 2019 2;2019:6051939. Epub 2019 Jan 2.

Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. Read More

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Inverting the Generator of a Generative Adversarial Network.

IEEE Trans Neural Netw Learn Syst 2018 Nov 2. Epub 2018 Nov 2.

Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are synthesized by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties that may be useful for downstream tasks such as classification or retrieval. Read More

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November 2018

Human sensitivity to perturbations constrained by a model of the natural image manifold.

J Vis 2018 10;18(11):20

Department of Psychology York University, Toronto, Ontario, Canada.

Humans are remarkably well tuned to the statistical properties of natural images. However, quantitative characterization of processing within the domain of natural images has been difficult because most parametric manipulations of a natural image make that image appear less natural. We used generative adversarial networks (GANs) to constrain parametric manipulations to remain within an approximation of the manifold of natural images. Read More

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October 2018

On the Effectiveness of Least Squares Generative Adversarial Networks.

IEEE Trans Pattern Anal Mach Intell 2019 12 24;41(12):2947-2960. Epub 2018 Sep 24.

Unsupervised learning with generative adversarial networks (GANs) has proven to be hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. Read More

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December 2019

Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer.

IEEE Trans Image Process 2019 Feb 12;28(2):546-560. Epub 2018 Sep 12.

Style transfer describes the rendering of an image's semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. Read More

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February 2019

Internists' dilemmas in their interactions with chronically ill patients; A comparison of their interaction strategies and dilemmas in two different medical contexts.

PLoS One 2018 30;13(5):e0194133. Epub 2018 May 30.

University of Groningen, University Medical Center Groningen, Wenckebach Institute, Groningen, The Netherlands.

Background: Internists appear to define productive interactions, key concept of the Chronic Care Model, as goal-directed, catalyzed by achieving rapport, and depending on the medical context: i.e. medically explained symptoms (MES) or medically unexplained symptoms (MUS). Read More

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Successful treatment of azole-resistant invasive aspergillosis in a bottlenose dolphin with high-dose posaconazole.

Med Mycol Case Rep 2017 Jun 1;16:16-19. Epub 2017 Apr 1.

Department of Medical Microbiology, Radboud University Medical Center, Nijmegen, The Netherlands.

Invasive aspergillosis due to azole-resistant is difficult to manage. We describe a case of azole-resistant invasive aspergillosis in a female bottlenose dolphin, who failed to respond to voriconazole and posaconazole therapy. As intravenous therapy was precluded, high dose posaconazole was initiated aimed at achieving trough levels exceeding 3 mg/l. Read More

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High-resolution vacuum-ultraviolet photoabsorption spectra of 1-butyne and 2-butyne.

J Chem Phys 2015 Jul;143(3):034304

Argonne National Laboratory, Argonne, Illinois 60439, USA.

The absolute photoabsorption cross sections of 1- and 2-butyne have been recorded at high resolution by using the vacuum-ultraviolet Fourier-Transform spectrometer at the SOLEIL Synchrotron. Both spectra show more resolved structure than previously observed, especially in the case of 2-butyne. In this work, we assess the potential importance of Rydberg states with higher values of orbital angular momentum, l, than are typically observed in photoabsorption experiments from ground state molecules. Read More

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[Plain abdominal x-rays for the sake of the doctor].

Ned Tijdschr Geneeskd 2014 ;158:A7493

Academisch Medisch Centrum, Amsterdam.

Scientific research has demonstrated that the diagnostic accuracy of plain abdominal x-rays is lower than that of other imaging modalities such as CT or ultrasonography in patients with acute abdominal pain. Nevertheless, plain x-rays are continually being used in daily practice. There are several comparable examples in which the implementation of new evidence into clinical practice seems problematic. Read More

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December 2014

Availability, affordability, and accessibility of a healthful diet in a low-income community, Central Falls, Rhode Island, 2007-2008.

Prev Chronic Dis 2010 Mar 15;7(2):A43. Epub 2010 Feb 15.

Marissa Sheldon, University of North Carolina, School of Public Health, Chapel Hill, North Carolina, USA.

Background: Many Americans have diets that do not meet the dietary guidelines set by the US Department of Agriculture (USDA). Additionally, low-income people have the highest rates of obesity and have difficulty accessing the necessary foods for maintaining a healthful diet.

Context: In December 2007 and January 2008, 21 retail food stores in Central Falls, Rhode Island, where residents were predominantly low-income Hispanics, were evaluated for the availability and costs of foods that fulfill the USDA's Thrifty Food Plan (TFP) guidelines. Read More

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Community-acquired bacterial meningitis in adults.

Pract Neurol 2008 Feb;8(1):8-23

Department of Neurology, Centre of Infection and Immunity Amsterdam (CINIMA), Academic Medical Centre, Amsterdam, The Netherlands.

Despite the availability of effective antibiotics, vaccination programmes and skilled acute-care facilities, there is still a significant mortality and morbidity from bacterial meningitis. Neurologists are often called on to "rule out bacterial meningitis", which can be difficult with the history and physical examination alone. In this review the authors will discuss the epidemiology, diagnosis and treatment of acute community-acquired bacterial meningitis in adults, focussing particularly on the management of patients with neurological complications, and stressing the importance of adjunctive dexamethasone. Read More

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February 2008

Fossil sister group of craniates: predicted and found.

J Morphol 2003 Oct;258(1):1-31

School of Biological Sciences, Washington State University, Pullman, Washington 99164-4236, USA.

This study investigates whether the recently described Cambrian fossil Haikouella (and the very similar Yunnanozoon) throws light on the longstanding problem of the origin of craniates. In the first rigorous cladistic analysis of the relations of this animal, we took 40 anatomical characters from Haikouella and other taxa (hemichordates, tunicates, cephalochordates, conodont craniates and other craniates, plus protostomes as the outgroup) and subjected these characters to parsimony analysis. The characters included several previously unrecognized traits of Haikouella, such as upper lips resembling those of larval lampreys, the thick nature of the branchial bars, a mandibular branchial artery but no mandibular branchial bar, muscle fibers defining the myomeres, a dark fibrous sheath that defines the notochord, conclusive evidence for paired eyes, and a large hindbrain and diencephalon in the same positions as in the craniate brain. Read More

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October 2003

[Imaging diagnosis of esophageal carcinoma by computed tomography and magnetic resonance imaging].

Nihon Geka Gakkai Zasshi 2002 Apr;103(4):331-6

Department of Diagnostic Radiology, Toranomon Hospital, Tokyo, Japan.

The staging diagnosis of esophageal carcinoma is important to determine therapeutic modalities and to predict prognosis. The current status of imaging diagnosis of tumor invasion to the adjacent organs and lymph node metastasis is described. The diagnostic criteria used to determine tumor invasion to the adjacent or gans by computed tomography (CT) and magnetic resonance imaging(MRI) are displacement and compression deformity of the tracheobronchial tree and obliteration of the periaortic fat plane over more than 90 degrees of the aortic circumference. Read More

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Difficult patients: their construction in group therapy.

J S Gans A Alonso

Int J Group Psychother 1998 Jul;48(3):311-26; discussion 327-45

Harvard Medical School, USA.

Written from the perspective of intersubjective theory, this article addresses how the leader and group members co-construct the difficult patient. Too often, therapists and patients have tended to attribute difficulties in therapy groups to "the difficult patient" without appreciating how they themselves contribute to the construction, the needs this construction serves, and the potential value of such patients to the group. Mistakes in group leadership, vicissitudes of intersubjectivity, disturbing intrapsychic defenses, and whole-group dynamics interact to produce the difficult patient. Read More

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