Generative adversarial networks: a renewal for data augmentation in lung nodule classification

Obtaining medical images is an ethically restrictive process and still difﬁcult to validate, depending on well-trained professional, being a laborious and time-consuming activity. Therefore, the construction of large databases of structured medical images is one of the major challenges of the deep learning applications in the computerized aid to the diagnosis in medical images. GAN presents itself as an adequate solution to supply the small number of pathological exams that compose the most diverse medical images banks. In this work we intend to develop a method using GAN to balance the data set of Computed Tomography images and improve the performance of an arbitrary classiﬁer of pulmonary nodules. For this, two GAN architectures with the capacity to generate synthetic images of nodal cuts were trained and in a second moment, a Convolutional Neural Network was trained in rooms of different data set. The training of the different data sets were evaluated by AUC-ROC.

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Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-2193 ◽

2021 ◽

Vol 7 (2) ◽

pp. 755-758

Author(s):

Daniel Wulff ◽

Mohamad Mehdi ◽

Floris Ernst ◽

Jannis Hagenah

Keyword(s):

Data Augmentation ◽

Ultrasound Image ◽

Image Data ◽

Representation Learning ◽

Generative Adversarial Networks ◽

Data Sets ◽

Imaging Data ◽

Data Set ◽

Adversarial Networks ◽

Classical Image

Abstract Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data. Another approach is to utilize learning-based augmentation methods, e.g. based on variational autoencoders or generative adversarial networks. However, a large amount of data is necessary to train these models, which is typically not available in scenarios where data augmentation is needed. One solution for this problem could be a transfer of augmentation models between different medical imaging data sets. In this work, we present a qualitative study of the cross data set generalization performance of different learning-based augmentation methods for ultrasound image data. We could show that knowledge transfer is possible in ultrasound image augmentation and that the augmentation partially results in semantically meaningful transfers of structures, e.g. vessels, across domains.

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Generating Scenery Images with Larger Variety According to User Descriptions

Applied Sciences ◽

10.3390/app112110224 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10224

Author(s):

Hsu-Yung Cheng ◽

Chih-Chang Yu

Keyword(s):

Neural Network ◽

Data Augmentation ◽

Memory Cell ◽

Generative Adversarial Networks ◽

Image Generation ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Adversarial Networks ◽

Hidden Layer

In this paper, a framework based on generative adversarial networks is proposed to perform nature-scenery generation according to descriptions from the users. The desired place, time and seasons of the generated scenes can be specified with the help of text-to-image generation techniques. The framework improves and modifies the architecture of a generative adversarial network with attention models by adding the imagination models. The proposed attentional and imaginative generative network uses the hidden layer information to initialize the memory cell of the recurrent neural network to produce the desired photos. A data set containing different categories of scenery images is established to train the proposed system. The experiments validate that the proposed method is able to increase the quality and diversity of the generated images compared to the existing method. A possible application of road image generation for data augmentation is also demonstrated in the experimental results.

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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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