Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification

Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score.

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A Study on the Improvement of Fine-grained Ship Classification through Data Augmentation Using Generative Adversarial Networks

10.1109/ictc52510.2021.9620888 ◽

2021 ◽

Author(s):

SungWon Moon ◽

Jiwon Lee ◽

Jungsoo Lee ◽

Ah Reum Oh ◽

Dowon Nam ◽

...

Keyword(s):

Data Augmentation ◽

Generative Adversarial Networks ◽

Fine Grained ◽

Adversarial Networks ◽

Ship Classification

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Indoor Localization using Data Augmentation via Selective Generative Adversarial Networks

IEEE Access ◽

10.1109/access.2021.3095546 ◽

2021 ◽

pp. 1-1

Author(s):

Wafa Njima ◽

Marwa Chafii ◽

Arsenia Chorti ◽

Raed M. Shubair ◽

H. Vincent Poor

Keyword(s):

Data Augmentation ◽

Indoor Localization ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Using Data

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Generative inter-class transformations for imbalanced data weather classification

London Imaging Meeting ◽

10.2352/issn.2694-118x.2021.lim-16 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 16-20

Author(s):

Apostolia Tsirikoglou ◽

Marcus Gladh ◽

Daniel Sahlin ◽

Gabriel Eilertsen ◽

Jonas Unger

Keyword(s):

Data Augmentation ◽

Imbalanced Data ◽

Training Data ◽

Generative Adversarial Networks ◽

Medical Applications ◽

Single Image ◽

Adversarial Networks ◽

Augmentation Strategies ◽

Future Work ◽

Small Dataset

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.

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Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks

Artificial Neural Networks and Machine Learning – ICANN 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-01424-7_58 ◽

2018 ◽

pp. 594-603 ◽

Cited By ~ 32

Author(s):

Antreas Antoniou ◽

Amos Storkey ◽

Harrison Edwards

Keyword(s):

Data Augmentation ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Using Data

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Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.723843 ◽

2021 ◽

Vol 15 ◽

Author(s):

Guangcheng Bao ◽

Bin Yan ◽

Li Tong ◽

Jun Shu ◽

Linyuan Wang ◽

...

Keyword(s):

Emotion Recognition ◽

Data Augmentation ◽

Generative Models ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Training Samples ◽

Using Data ◽

Effective Models

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of training samples, which makes it difficult to establish effective models for emotion recognition. Inspired by the excellent achievements of generative models in image processing, we propose a data augmentation model named VAE-D2GAN for EEG-based emotion recognition using a generative adversarial network. EEG features representing different emotions are extracted as topological maps of differential entropy (DE) under five classical frequency bands. The proposed model is designed to learn the distributions of these features for real EEG signals and generate artificial samples for training. The variational auto-encoder (VAE) architecture can learn the spatial distribution of the actual data through a latent vector, and is introduced into the dual discriminator GAN to improve the diversity of the generated artificial samples. To evaluate the performance of this model, we conduct a systematic test on two public emotion EEG datasets, the SEED and the SEED-IV. The obtained recognition accuracy of the method using data augmentation shows as 92.5 and 82.3%, respectively, on the SEED and SEED-IV datasets, which is 1.5 and 3.5% higher than that of methods without using data augmentation. The experimental results show that the artificial samples generated by our model can effectively enhance the performance of the EEG-based emotion recognition.

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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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