Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method was evaluated on the segmentation of chest radiographic images, showing promising results. The multi-stage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach.

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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

Informatics in Medicine Unlocked ◽

10.1016/j.imu.2021.100779 ◽

2021 ◽

pp. 100779

Author(s):

Saman Motamed ◽

Patrik Rogalla ◽

Farzad Khalvati

Keyword(s):

Data Augmentation ◽

Generative Adversarial Networks ◽

X Ray ◽

Adversarial Networks ◽

Chest X Ray

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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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Medical Data Augmentation Using Generative Adversarial Networks : X-ray Image Generation for Transfer Learning of Hip Fracture Detection

2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI) ◽

10.1109/taai48200.2019.8959908 ◽

2019 ◽

Author(s):

Ying-Jia Lin ◽

I-Fang Chung

Keyword(s):

Hip Fracture ◽

Transfer Learning ◽

Data Augmentation ◽

Medical Data ◽

Generative Adversarial Networks ◽

Image Generation ◽

Fracture Detection ◽

X Ray ◽

Adversarial Networks

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Comparison of GANs for Covid-19 X-ray classification

10.5753/eniac.2021.18238 ◽

2021 ◽

Author(s):

Luiz Felipe Cavalcanti ◽

Lilian Berton

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Original Data ◽

Machine Learning Algorithms ◽

Generative Adversarial Networks ◽

Artificial Data ◽

X Ray ◽

Adversarial Networks ◽

Radiological Images

Image classification has been applied to several real problems. However, getting labeled data is a costly task, since it demands time, resources and experts. Furthermore, some domains like disease detection suffer from unbalanced classes. These scenarios are challenging and degrade the performance of machine learning algorithms. In these cases, we can use Data Augmentation (DA) approaches to increase the number of labeled examples in a dataset. The objective of this work is to analyze the use of Generative Adversarial Networks (GANs) as DA, which are capable of synthesizing artificial data from the original data, under an adversarial process of two neural networks. The GANs are applied in the classification of unbalanced Covid-19 radiological images. Increasing the number of images led to better accuracy for all the GANs tested, especially in the multi-label dataset, mitigating the bias for unbalanced classes.

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Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses through Deep Belief Network with Data Augmentation using Conditional Generative Adversarial Networks

IEEE Access ◽

10.1109/access.2021.3096039 ◽

2021 ◽

pp. 1-1

Author(s):

Nusrat Shaheen ◽

Basit Raza ◽

Ahmad Raza Shahid ◽

Ahmad Kamran Malik

Keyword(s):

Large Scale ◽

Performance Modeling ◽

Data Augmentation ◽

Deep Belief Network ◽

Generative Adversarial Networks ◽

Data Warehouses ◽

Belief Network ◽

Adversarial Networks

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Data Augmentation Using Generative Adversarial Networks for Electrical Insulator Anomaly Detection

Proceedings of the 2020 2nd International Conference on Management Science and Industrial Engineering ◽

10.1145/3396743.3396790 ◽

2020 ◽

Author(s):

Lei Luo ◽

William Hsu ◽

Shangxian Wang

Keyword(s):

Anomaly Detection ◽

Data Augmentation ◽

Generative Adversarial Networks ◽

Electrical Insulator ◽

Adversarial Networks

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Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

Applied Sciences ◽

10.3390/app11146368 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6368

Author(s):

Fátima A. Saiz ◽

Garazi Alfaro ◽

Iñigo Barandiaran ◽

Manuel Graña

Keyword(s):

Ad Hoc ◽

Data Augmentation ◽

Semantic Network ◽

Semantic Networks ◽

Stereo Image ◽

Generative Adversarial Networks ◽

Specific Class ◽

Adversarial Networks ◽

Augmentation Techniques ◽

Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

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