scholarly journals Generating Virtual Short Tau Inversion Recovery (STIR) Images from T1- and T2-Weighted Images Using a Conditional Generative Adversarial Network in Spine Imaging

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1542
Author(s):  
Johannes Haubold ◽  
Aydin Demircioglu ◽  
Jens Matthias Theysohn ◽  
Axel Wetter ◽  
Alexander Radbruch ◽  
...  
Keyword(s):  
Inversion Recovery ◽  
Training Dataset ◽  
Validation Dataset ◽  
Generative Adversarial Network ◽  
Diagnostic Quality ◽  
Adversarial Network ◽  
Scanning Time ◽  
T1 And T2 ◽  
Short Tau Inversion Recovery ◽  
T2 Weighted Images

Short tau inversion recovery (STIR) sequences are frequently used in magnetic resonance imaging (MRI) of the spine. However, STIR sequences require a significant amount of scanning time. The purpose of the present study was to generate virtual STIR (vSTIR) images from non-contrast, non-fat-suppressed T1- and T2-weighted images using a conditional generative adversarial network (cGAN). The training dataset comprised 612 studies from 514 patients, and the validation dataset comprised 141 studies from 133 patients. For validation, 100 original STIR and respective vSTIR series were presented to six senior radiologists (blinded for the STIR type) in independent A/B-testing sessions. Additionally, for 141 real or vSTIR sequences, the testers were required to produce a structured report of 15 different findings. In the A/B-test, most testers could not reliably identify the real STIR (mean error of tester 1–6: 41%; 44%; 58%; 48%; 39%; 45%). In the evaluation of the structured reports, vSTIR was equivalent to real STIR in 13 of 15 categories. In the category of the number of STIR hyperintense vertebral bodies (p = 0.08) and in the diagnosis of bone metastases (p = 0.055), the vSTIR was only slightly insignificantly equivalent. By virtually generating STIR images of diagnostic quality from T1- and T2-weighted images using a cGAN, one can shorten examination times and increase throughput.

scholarly journals Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set

2020 ◽  
Vol 11 ◽  
Author(s):  
Luning Bi ◽  
Guiping Hu
Keyword(s):  
Classification Accuracy ◽  
Plant Disease ◽  
Data Augmentation ◽  
Plant Diseases ◽  
Disease Classification ◽  
Training Data ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Overfitting Problem

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.

scholarly journals Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning

2021 ◽  
Vol 11 (16) ◽  
pp. 7489
Author(s):  
Mohammed Salah Al-Radhi ◽  
Tamás Gábor Csapó ◽  
Géza Németh
Keyword(s):  
Speech Processing ◽  
Voice Conversion ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Sinusoidal Model ◽  
Time Alignment ◽  
Adversarial Network ◽  
Training Examples ◽  
Speaking Style ◽  
Target Speaker

Voice conversion (VC) transforms the speaking style of a source speaker to the speaking style of a target speaker by keeping linguistic information unchanged. Traditional VC techniques rely on parallel recordings of multiple speakers uttering the same sentences. Earlier approaches mainly find a mapping between the given source–target speakers, which contain pairs of similar utterances spoken by different speakers. However, parallel data are computationally expensive and difficult to collect. Non-parallel VC remains an interesting but challenging speech processing task. To address this limitation, we propose a method that allows a non-parallel many-to-many voice conversion by using a generative adversarial network. To the best of the authors’ knowledge, our study is the first one that employs a sinusoidal model with continuous parameters to generate converted speech signals. Our method involves only several minutes of training examples without parallel utterances or time alignment procedures, where the source–target speakers are entirely unseen by the training dataset. Moreover, empirical study is carried out on the publicly available CSTR VCTK corpus. Our conclusions indicate that the proposed method reached the state-of-the-art results in speaker similarity to the utterance produced by the target speaker, while suggesting important structural ones to be further analyzed by experts.

scholarly journals Generating the Microstructure of Al-Si Cast Alloys Using Machine Learning

2021 ◽  
Vol 59 (11) ◽  
pp. 838-847
Author(s):  
In-Kyu Hwang ◽  
Hyun-Ji Lee ◽  
Sang-Jun Jeong ◽  
In-Sung Cho ◽  
Hee-Soo Kim
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Training Dataset ◽  
Initial Training ◽  
Microstructural Characteristics ◽  
Generative Adversarial Network ◽  
The Real ◽  
Adversarial Network ◽  
Cast Alloys ◽  
The Right

In this study, we constructed a deep convolutional generative adversarial network (DCGAN) to generate the microstructural images that imitate the real microstructures of binary Al-Si cast alloys. We prepared four combinations of alloys, Al-6wt%Si, Al-9wt%Si, Al-12wt%Si and Al-15wt%Si for machine learning. DCGAN is composed of a generator and a discriminator. The discriminator has a typical convolutional neural network (CNN), and the generator has an inverse shaped CNN. The fake images generated using DCGAN were similar to real microstructural images. However, they showed some strange morphology, including dendrites without directionality, and deformed Si crystals. Verification with Inception V3 revealed that the fake images generated using DCGAN were well classified into the target categories. Even the visually imperfect images in the initial training iterations showed high similarity to the target. It seems that the imperfect images had enough microstructural characteristics to satisfy the classification, even though human cannot recognize the images. Cross validation was carried out using real, fake and other test images. When the training dataset had the fake images only, the real and test images showed high similarities to the target categories. When the training dataset contained both the real and fake images, the similarity at the target categories were high enough to meet the right answers. We concluded that the DCGAN developed for microstructural images in this study is highly useful for data augmentation for rare microstructures.

scholarly journals Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Saeed Karimi-Bidhendi ◽  
Arghavan Arafati ◽  
Andrew L. Cheng ◽  
Yilei Wu ◽  
Arash Kheradvar ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Training Dataset ◽  
Clinical Workflow ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network ◽  
Automated Method

Abstract Background For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging offers non-invasive and non-ionizing assessment of CHD patients. However, although CMR data facilitates reliable analysis of cardiac function and anatomy, clinical workflow mostly relies on manual analysis of CMR images, which is time consuming. Thus, an automated and accurate segmentation platform exclusively dedicated to pediatric CMR images can significantly improve the clinical workflow, as the present work aims to establish. Methods Training artificial intelligence (AI) algorithms for CMR analysis requires large annotated datasets, which are not readily available for pediatric subjects and particularly in CHD patients. To mitigate this issue, we devised a novel method that uses a generative adversarial network (GAN) to synthetically augment the training dataset via generating synthetic CMR images and their corresponding chamber segmentations. In addition, we trained and validated a deep fully convolutional network (FCN) on a dataset, consisting of $$64$$ 64 pediatric subjects with complex CHD, which we made publicly available. Dice metric, Jaccard index and Hausdorff distance as well as clinically-relevant volumetric indices are reported to assess and compare our platform with other algorithms including U-Net and cvi42, which is used in clinics. Results For congenital CMR dataset, our FCN model yields an average Dice metric of $$91.0\mathrm{\%}$$ 91.0 % and $$86.8\mathrm{\%}$$ 86.8 % for LV at end-diastole and end-systole, respectively, and $$84.7\mathrm{\%}$$ 84.7 % and $$80.6\mathrm{\%}$$ 80.6 % for RV at end-diastole and end-systole, respectively. Using the same dataset, the cvi42, resulted in $$73.2\mathrm{\%}$$ 73.2 % , $$71.0\mathrm{\%}$$ 71.0 % , $$54.3\mathrm{\%}$$ 54.3 % and $$53.7\mathrm{\%}$$ 53.7 % for LV and RV at end-diastole and end-systole, and the U-Net architecture resulted in $$87.4\mathrm{\%}$$ 87.4 % , $$83.9\mathrm{\%}$$ 83.9 % , $$81.8\mathrm{\%}$$ 81.8 % and $$74.8\mathrm{\%}$$ 74.8 % for LV and RV at end-diastole and end-systole, respectively. Conclusions The chambers’ segmentation results from our fully-automated method showed strong agreement with manual segmentation and no significant statistical difference was found by two independent statistical analyses. Whereas cvi42 and U-Net segmentation results failed to pass the t-test. Relying on these outcomes, it can be inferred that by taking advantage of GANs, our method is clinically relevant and can be used for pediatric and congenital CMR segmentation and analysis.

scholarly journals RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images

2022 ◽  
Vol 14 (2) ◽  
pp. 251
Author(s):  
Yuanzheng Wang ◽  
Hui Qin ◽  
Yu Tang ◽  
Donghao Zhang ◽  
Donghui Yang ◽  
...  
Keyword(s):  
Electromagnetic Waves ◽  
Network Performance ◽  
Tunnel Lining ◽  
Shielding Effect ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
High Level ◽  
Ground Penetrating ◽  
Performance Validation

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Improving the Classification Rate of Insulation Defects in Power Cables by Automatic Features Extraction and Sample Augmentation of Partial Discharge Signal

2021 ◽  
Author(s):  
Kang Sun ◽  
Xuyang Xuan ◽  
Laijun Zhao ◽  
Jie Long
Keyword(s):  
Time Window ◽  
Partial Discharge ◽  
Image Data ◽  
Features Extraction ◽  
Training Dataset ◽  
Power Cables ◽  
Generative Adversarial Network ◽  
Classification Rate ◽  
Adversarial Network ◽  
Pattern Recognition Methods

Conventional pattern recognition methods employed for differentiating the types of insulation defects in power cables usually rely on the manual extraction of partial discharge features, which is inefficient and easily affected by subjective uncertainty. This work addresses this problem by proposing a new framework based on the automatic features extraction of partial discharge signal. The method first applies a sliding time window to convert partial discharge signals in the time domain into two-dimensional images that serve directly as the input to the convolutional neural networks (CNNs). Then a nonlinear encoder is employed to automatically extract the features of the partial discharge image data as the input of CNNs for classification. In addition, we address the overfitting problem associated with the few-shot by applying a deep convolutional generative adversarial network (DCGAN) to augment the original training dataset. Experimental results demonstrate the validity of the proposed algorithm; it increases the classification accuracy by 4.18% relative to that achieved with manually extracted features; the overall accuracy of the proposed algorithm training with the augmented dataset is 3.175% higher than that with the original experimental dataset.

Reconstruction of Irregular Missing Seismic Data Using Conditional Generative Adversarial Networks

Geophysics ◽  
2021 ◽  
pp. 1-154
Author(s):  
Qing Wei ◽  
xiangyang Li ◽  
Mingpeng Song
Keyword(s):  
Missing Data ◽  
Gaussian Noise ◽  
Seismic Data ◽  
Interpolation Method ◽  
Synthetic Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Datasets

During acquisition, due to economic and natural reasons, irregular missing seismic data are always observed. To improve accuracy in subsequent processing, the missing data should be interpolated. A conditional generative adversarial network (cGAN) consisting of two networks, a generator and a discriminator, is a deep learning model that can be used to interpolate the missing data. However, because cGAN is typically dataset-oriented, the trained network is unable to interpolate a dataset from an area different from that of the training dataset. We design a cGAN based on Pix2Pix GAN to interpolate irregular missing seismic data. A synthetic dataset synthesized from two models is used to train the network. Further, we add a Gaussian-noise layer in the discriminator to fix a vanishing gradient, allowing us to train a more powerful generator. Two synthetic datasets synthesized by two new geological models and two field datasets are used to test the trained cGAN. The test results and the calculated recovered signal-to-noise ratios indicate that although the cGAN is trained using synthetic data, the network can reconstruct irregular missing field seismic data with high accuracy using the Gaussian-noise layer. We test the performances of cGANs trained with different patch sizes in the discriminator to determine the best structure, and we train the networks using different training datasets for different missing rates, demonstrating the best training dataset. Compared with conventional methods, the cGAN based interpolation method does not need different parameter selections for different datasets to obtain the best interpolation data. Furthermore, it is also an efficient technique as the cost is because of the training, and after training, the processing time is negligible.

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