scholarly journals Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

2020 ◽  
Vol 6 (9) ◽  
pp. 83 ◽  
Author(s):  
Ufuk Cem Birbiri ◽  
Azam Hamidinekoo ◽  
Amélie Grall ◽  
Paul Malcolm ◽  
Reyer Zwiggelaar
Keyword(s):  
Region Of Interest ◽  
Training Data ◽  
Prostate Tissue ◽  
Generative Adversarial Networks ◽  
Correct Identification ◽  
Adversarial Networks ◽  
Training Samples ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Public Datasets

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

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

2020 ◽  
Vol 34 (03) ◽  
pp. 2645-2652 ◽  
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.

scholarly journals Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks

2018 ◽  
Author(s):  
Matthias Häring ◽  
Jörg Großhans ◽  
Fred Wolf ◽  
Stephan Eule
Keyword(s):  
Ground Truth ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Epithelial Tissue ◽  
Automated Segmentation ◽  
Segmentation Method ◽  
Adversarial Networks ◽  
Training Samples ◽  
Segmentation Of Images ◽  
Image Mask

AbstractA central problem in biomedical imaging is the automated segmentation of images for further quantitative analysis. Recently, fully convolutional neural networks, such as the U-Net, were applied successfully in a variety of segmentation tasks. A downside of this approach is the requirement for a large amount of well-prepared training samples, consisting of image - ground truth mask pairs. Since training data must be created by hand for each experiment, this task can be very costly and time-consuming. Here, we present a segmentation method based on cycle consistent generative adversarial networks, which can be trained even in absence of prepared image - mask pairs. We show that it successfully performs image segmentation tasks on samples with substantial defects and even generalizes well to different tissue types.

GANDaLF: GAN for Data-Limited Fingerprinting

2021 ◽  
Vol 2021 (2) ◽  
pp. 305-322
Author(s):  
Se Eun Oh ◽  
Nate Mathews ◽  
Mohammad Saidur Rahman ◽  
Matthew Wright ◽  
Nicholas Hopper
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Large Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Closed World ◽  
Training Samples ◽  
A Site

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

Semi-Supervised Learning-Based Live Fish Identification in Aquaculture Using Modified Deep Convolutional Generative Adversarial Networks

2018 ◽  
Vol 61 (2) ◽  
pp. 699-710 ◽  
Author(s):  
Jian Zhao ◽  
Yihao Li ◽  
Fengdeng Zhang ◽  
Songming Zhu ◽  
Ying Liu ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Ground Truth ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Adversarial Networks ◽  
Live Fish ◽  
Training Samples ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Abstract. Aiming at live fish identification in aquaculture, a practical and efficient semi-supervised learning model, based on modified deep convolutional generative adversarial networks (DCGANs), was proposed in this study. Benefiting from the modified DCGANs structure, the presented model can be trained effectively using relatively few labeled training samples. In consideration of the complex poses of fish and the low resolution of sampling images in aquaculture, spatial pyramid pooling and some improved techniques specifically for the presented model were used to make the model more robust. Finally, in tests with two preprocessed and challenging datasets (with 5%, 10%, and 15% labeled training data in the fish recognition ground-truth dataset and 25%, 50%, and 75% labeled training data in the Croatian fish dataset), the feasibility and reliability of the presented model for live fish identification were proved with respective accuracies of 80.52%, 81.66%, and 83.07% for the ground-truth dataset and 65.13%, 78.72%, and 82.95% for the Croatian fish dataset. Keywords: Aquaculture, Deep convolutional generative adversarial networks, Few labeled training samples, Live fish identification, Semi-supervised learning, Spatial pyramid pooling.

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

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.

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

Linear electromagnetic inverse scattering via generative adversarial networks

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.

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

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.

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

2019 ◽  
Vol 8 (9) ◽  
pp. 390 ◽  
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.

Shale Digital Core Image Generation Based on Generative Adversarial Networks

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Wenshu Zha ◽  
Xingbao Li ◽  
Daolun Li ◽  
Yan Xing ◽  
Lei He ◽  
...  
Keyword(s):  
Organic Matter ◽  
Morphological Characteristics ◽  
Generative Adversarial Networks ◽  
Reconstruction Method ◽  
Core Samples ◽  
Adversarial Networks ◽  
Training Samples ◽  
Digital Core ◽  
Artificial Intelligence Models ◽  
Convolution Properties

Abstract Stochastic reconstruction of digital core images is a vital part of digital core physics analysis, aiming to generate representative microstructure samples for sampling and uncertainty quantification analysis. This paper proposes a novel reconstruction method of the digital core of shale based on generative adversarial networks (GANs) with powerful capabilities of the generation of samples. GANs are a series of unsupervised generative artificial intelligence models that take the noise vector as an input. In this paper, the GANs with a generative and a discriminative network are created respectively, and the shale image with 45 nm/pixel preprocessed by the three-value-segmentation method is used as training samples. The generative network is used to learn the distribution of real training samples, and the discriminative network is used to distinguish real samples from synthetic ones. Finally, realistic digital core samples of shale are successfully reconstructed through the adversarial training process. We used the Fréchet inception distance (FID) and Kernel inception distance (KID) to evaluate the ability of GANs to generate real digital core samples of shale. The comparison of the morphological characteristics between them, such as the ratio of organic matter and specific surface area of organic matter, indicates that real and reconstructed samples are highly close. The results show that deep convolutional generative adversarial networks with full convolution properties can reconstruct digital core samples of shale effectively. Therefore, compared with the classical methods of reconstruction, the new reconstruction method is more promising.

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