Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography ( CT ) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data ( SCLLD ) relying on Sobel edge detection and Generative Adversarial Networks ( GANs ) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network ( CNN ) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 ± 0.20%, 99.88 ± 0.24%, and 99.40 ± 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 ± 4.11%, 91.2 ± 6.15%, and 46.40 ± 5.21%.

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Data Augmentation Using Generative Adversarial Networks (GANs) For GAN-Based Detection Of Pneumonia And COVID-19 In Chest X-Ray Images

10.21203/rs.3.rs-146161/v1 ◽

2021 ◽

Author(s):

Saman Motamed ◽

Patrik Rogalla ◽

Farzad Khalvati

Keyword(s):

Neural Networks ◽

Data Augmentation ◽

Generative Models ◽

Training Data ◽

Generative Adversarial Networks ◽

X Rays ◽

X Ray ◽

Standard Data ◽

Adversarial Networks ◽

Augmentation Techniques

Abstract Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

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Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs13091713 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1713

Author(s):

Songwei Gu ◽

Rui Zhang ◽

Hongxia Luo ◽

Mengyao Li ◽

Huamei Feng ◽

...

Keyword(s):

Remote Sensing ◽

Real Life ◽

Attention Mechanism ◽

Training Data ◽

Generative Adversarial Networks ◽

Natural Image ◽

Remote Sensing Images ◽

Training Time ◽

Adversarial Networks ◽

Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

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Linear electromagnetic inverse scattering via generative adversarial networks

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078721001331 ◽

2021 ◽

pp. 1-9

Author(s):

Huilin Zhou ◽

Huimin Zheng ◽

Qiegen Liu ◽

Jian Liu ◽

Yuhao Wang

Keyword(s):

Inverse Scattering ◽

Optimization Methods ◽

Training Data ◽

Generative Adversarial Networks ◽

Scattering Problems ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Highly Nonlinear ◽

Electromagnetic Inverse Scattering

Abstract Electromagnetic inverse-scattering problems (ISPs) are concerned with determining the properties of an unknown object using measured scattered fields. ISPs are often highly nonlinear, causing the problem to be very difficult to address. In addition, the reconstruction images of different optimization methods are distorted which leads to inaccurate reconstruction results. To alleviate these issues, we propose a new linear model solution of generative adversarial network-based (LM-GAN) inspired by generative adversarial networks (GAN). Two sub-networks are trained alternately in the adversarial framework. A linear deep iterative network as a generative network captures the spatial distribution of the data, and a discriminative network estimates the probability of a sample from the training data. Numerical results validate that LM-GAN has admirable fidelity and accuracy when reconstructing complex scatterers.

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Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration

Frontiers in Medicine ◽

10.3389/fmed.2021.794969 ◽

2022 ◽

Vol 8 ◽

Author(s):

Runnan He ◽

Shiqi Xu ◽

Yashu Liu ◽

Qince Li ◽

Yang Liu ◽

...

Keyword(s):

Medical Imaging ◽

Random Noise ◽

Three Dimensional ◽

Poor Quality ◽

Training Data ◽

Generative Adversarial Networks ◽

Liver Segmentation ◽

Deep Convolutional Neural Networks ◽

Adversarial Networks ◽

Liver Region

Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.

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Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090390 ◽

2019 ◽

Vol 8 (9) ◽

pp. 390 ◽

Cited By ~ 1

Author(s):

Kun Zheng ◽

Mengfei Wei ◽

Guangmin Sun ◽

Bilal Anas ◽

Yu Li

Keyword(s):

Remote Sensing ◽

Traffic Control ◽

Data Augmentation ◽

Vehicle Detection ◽

Training Data ◽

Generative Adversarial Networks ◽

Remote Sensing Images ◽

Adversarial Networks ◽

Vehicle Data ◽

Augmentation Strategy

Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks.

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Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i03.5649 ◽

2020 ◽

Vol 34 (03) ◽

pp. 2645-2652 ◽

Cited By ~ 2

Author(s):

Yaman Kumar ◽

Dhruva Sahrawat ◽

Shubham Maheshwari ◽

Debanjan Mahata ◽

Amanda Stent ◽

...

Keyword(s):

Speech Recognition ◽

Classification Problem ◽

Visual Speech ◽

Training Data ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Novel Approach ◽

Visual Speech Recognition ◽

Training Samples ◽

English Training

Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. Recent machine learning based approaches model VSR as a classification problem; however, the scarcity of training data leads to error-prone systems with very low accuracies in predicting unseen classes. To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases. We also show that our models are language agnostic and therefore capable of seamlessly generating, using English training data, videos for a new language (Hindi). To the best of our knowledge, this is the first work to show empirical evidence of the use of GANs for generating training samples of unseen classes in the domain of VSR, hence facilitating zero-shot learning. We make the added videos for new classes publicly available along with our code1.

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An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

Applied Sciences ◽

10.3390/app10155032 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5032

Author(s):

Xiaochang Wu ◽

Xiaolin Tian

Keyword(s):

Image Segmentation ◽

Medical Image Segmentation ◽

Chest Radiographs ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

X Ray ◽

Adversarial Network ◽

Cardiac Segmentation ◽

Adversarial Networks ◽

Feature Extractor

Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.

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Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

IEEE Access ◽

10.1109/access.2020.2992861 ◽

2020 ◽

Vol 8 ◽

pp. 86536-86544 ◽

Cited By ~ 3

Author(s):

Yue Zhu ◽

Yutao Zhang ◽

Haigang Zhang ◽

Jinfeng Yang ◽

Zihao Zhao

Keyword(s):

Data Augmentation ◽

Generative Adversarial Networks ◽

X Ray ◽

Adversarial Networks

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Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6723 ◽

2020 ◽

Vol 34 (07) ◽

pp. 10909-10916

Author(s):

Ligong Han ◽

Ruijiang Gao ◽

Mun Kim ◽

Xin Tao ◽

Bo Liu ◽

...

Keyword(s):

Generative Adversarial Networks ◽

Pairwise Comparisons ◽

Generative Adversarial Network ◽

Weak Supervision ◽

Adversarial Network ◽

Adversarial Networks ◽

Large Numbers ◽

Supervised Methods ◽

Adversarial Training ◽

Noise Tolerant

Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Code and Supplementary can be found on the project website*.

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Generative Adversarial Networks With Synthetic Training Data for Enforcing Manufacturing Constraints on Topology Optimization

Volume 11A: 46th Design Automation Conference (DAC) ◽

10.1115/detc2020-22399 ◽

2020 ◽

Author(s):

Michael Greminger

Keyword(s):

Topology Optimization ◽

Vector Space ◽

Training Data ◽

Generative Adversarial Networks ◽

Manufacturing Constraints ◽

Cnc Milling ◽

Manufacturing Method ◽

Latent Vector ◽

Adversarial Networks ◽

Manufacturing Methods

Abstract Topology optimization is a powerful tool to generate mechanical designs that use minimal mass to achieve their function. However, the designs obtained using topology optimization are often not manufacturable using a given manufacturing process. There exist some modifications to the traditional topology optimization algorithm that are able to impose manufacturing constraints for a limited set of manufacturing methods. These approaches have the drawback that they are often based on heuristics to obtain the manufacturability constraint and thus cannot be applied generally to multiple manufacturing methods. In order to create a general approach to imposing manufacturing constraints on topology optimization, generative adversarial networks (GANs) are used. GANs have the capability to produce samples from a distribution defined by training data. In this work, the GAN is trained by generating synthetic 3D voxel training data that represent the distribution of designs that can be created by a particular manufacturing method. Once trained, the GAN forms a mapping from a latent vector space to the space of manufacturable designs. The topology optimization is then performed on the latent vector space ensuring that the design obtained is manufacturable. The effectiveness of this approach is demonstrated by training a GAN on designs intended to be manufacturable on a 3-axis computer numerically controlled (CNC) milling machine.

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