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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. Epub 2021 Jun 8.

Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Gloriastrasse 35, 8092 Zürich, 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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Topology and Content Co-Alignment Graph Convolutional Learning.

IEEE Trans Neural Netw Learn Syst 2021 Jun 11;PP. Epub 2021 Jun 11.

In traditional graph neural networks (GNNs), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learning. In reality, network topology and node content each provide unique and important information, and they are not always consistent because of noise, irrelevance, or missing links between nodes. A pure topology-driven feature aggregation approach between unaligned neighborhoods may deteriorate learning from nodes with poor structure-content consistency, due to the propagation of incorrect messages over the whole network. Read More

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JPEG Robust Invertible Grayscale.

IEEE Trans Vis Comput Graph 2021 Jun 11;PP. Epub 2021 Jun 11.

Invertible grayscale is a special kind of grayscale from which the original color can be recovered. Given an input color image, this seminal work tries to hide the color information into its grayscale counterpart while making it hard to recognize any anomalies. This powerful functionality is enabled by training a hiding sub-network and restoring sub-network in an end-to-end way. Read More

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Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

Nat Biomed Eng 2021 Jun 10. Epub 2021 Jun 10.

Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Read More

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Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks.

BMC Med Inform Decis Mak 2021 Jun 11;21(1):186. Epub 2021 Jun 11.

Knowledgepark GmbH, Leonrodstr. 68, 80636, Munich, Germany.

Background: Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. Read More

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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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Adversarial Caching Training: Unsupervised Inductive Network Representation Learning on Large-Scale Graphs.

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

Network representation learning (NRL) has far-reaching effects on data mining research, showing its importance in many real-world applications. NRL, also known as network embedding, aims at preserving graph structures in a low-dimensional space. These learned representations can be used for subsequent machine learning tasks, such as vertex classification, link prediction, and data visualization. Read More

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Self-Training Enhanced: Network Embedding and Overlapping Community Detection With Adversarial Learning.

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

Network embedding (NE) aims to encode the relations of vertices into a low-dimensional space. After NE, we can obtain the learned vectors of vertices that preserve the proximity of network structures for subsequent applications, e.g. Read More

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Removing Adversarial Noise via Low-rank Completion of High-sensitivity Points.

IEEE Trans Image Process 2021 Jun 10;PP. Epub 2021 Jun 10.

Deep neural networks are fragile under adversarial attacks. In this work, we propose to develop a new defense method based on image restoration to remove adversarial attack noise. Using the gradient information back-propagated over the network to the input image, we identify high-sensitivity keypoints which have significant contributions to the image classification performance. Read More

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Deep Cross-User Models Reduce the Training Burden in Myoelectric Control.

Front Neurosci 2021 24;15:657958. Epub 2021 May 24.

Department of Electrical and Computer Engineering, Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada.

The effort, focus, and time to collect data and train EMG pattern recognition systems is one of the largest barriers to their widespread adoption in commercial applications. In addition to multiple repetitions of motions, including exemplars of confounding factors during the training protocol has been shown to be critical for robust machine learning models. This added training burden is prohibitive for most regular use cases, so cross-user models have been proposed that could leverage inter-repetition variability supplied by other users. 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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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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Deformable adversarial registration network with multiple loss constraints.

Comput Med Imaging Graph 2021 May 26;91:101931. Epub 2021 May 26.

Video Processing and Communication Laboratory, Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.

Deformable medical image registration has the necessary value of theoretical research and clinical application. Traditional methods cannot meet clinical application standards in terms of registration accuracy and efficiency. This article proposes a deformable generate adversarial registration framework, which avoids the dependence on ground-truth deformation. Read More

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Adversarial orthogonal regression: Two non-linear regressions for causal inference.

Neural Netw 2021 May 20;143:66-73. Epub 2021 May 20.

Department of Philosophy, Carnegie Mellon University, Pittsburgh, United States. Electronic address:

We propose two nonlinear regression methods, namely, Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) for the general case of structural equation models. Both methods try to make the residual of regression independent from regressors, while putting no assumption on noise distribution. In both methods, two adversarial networks are trained simultaneously where a regression network outputs predictions and a loss network that estimates mutual information (in AdOR) and KL-divergence (in AdOSE). Read More

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Learning-based dose prediction for pancreatic stereotactic body radiation therapy using dual pyramid adversarial network.

Phys Med Biol 2021 Jun 4. Epub 2021 Jun 4.

Department of Radiology and Sciences Imaging Department of Radiology Oncology, Emory University, Atlanta, Georgia, UNITED STATES.

Treatment planning for pancreatic cancer stereotactic body radiation therapy (SBRT) is very challenging owing to vast spatial variations and close proximity of many organs-at-risk. Recently, deep learning (DL)-based methods have been applied in dose prediction tasks of various treatment sites with the aim of relieving planning challenges. Limited investigations, however, have been reported on DL-based dose prediction for pancreatic cancer SBRT. 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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Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning.

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

Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA.

The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. 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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Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data.

Sensors (Basel) 2021 May 26;21(11). Epub 2021 May 26.

Department of Geoinformatics, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland.

With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solve this, we propose a novel three-staged deep adversarial neural network architecture capable of denoising and refining real-world depth sensor input for full human body posture reconstruction. 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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