scholarly journals Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

2021 ◽  
Vol 14 ◽  
Author(s):  
Eric Nathan Carver ◽  
Zhenzhen Dai ◽  
Evan Liang ◽  
James Snyder ◽  
Ning Wen
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Model Performance ◽  
Image Data ◽  
Generative Adversarial Networks ◽  
Dice Similarity Coefficient ◽  
Generative Adversarial Network ◽  
Post Contrast ◽  
Adversarial Network ◽  
Fluid Attenuated Inversion Recovery

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. MRI plays an essential role in the diagnosis and treatment assessment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigated the creation of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (Flair) MR images. These synthetic MR (synMR) images were assessed quantitatively with four metrics. The synMR images were also assessed qualitatively by an authoring physician with notions that synMR possessed realism in its portrayal of structural boundaries but struggled to accurately depict tumor heterogeneity. Additionally, this study investigated the synMR images created by generative adversarial network (GAN) to overcome the lack of annotated medical image data in training U-Nets to segment enhancing tumor, whole tumor, and tumor core regions on gliomas. Multiple two-dimensional (2D) U-Nets were trained with original BraTS data and differing subsets of the synMR images. Dice similarity coefficient (DSC) was used as the loss function during training as well a quantitative metric. Additionally, Hausdorff Distance 95% CI (HD) was used to judge the quality of the contours created by these U-Nets. The model performance was improved in both DSC and HD when incorporating synMR in the training set. In summary, this study showed the ability to generate high quality Flair, T2, T1, and T1CE synMR images using GAN. Using synMR images showed encouraging results to improve the U-Net segmentation performance and shows potential to address the scarcity of annotated medical images.

scholarly journals Correction to “Noise Adaptation Generative Adversarial Network for Medical Image Analysis”

2020 ◽  
Vol 39 (7) ◽  
pp. 2566-2567
Author(s):  
Tianyang Miller ◽  
Jun Cheng ◽  
Huazhu Fu ◽  
Zaiwang Gu ◽  
Yuting Xiao ◽  
...  
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

2021 ◽  
Vol 1 (1) ◽  
pp. 20-22
Author(s):  
Awadelrahman M. A. Ahmed ◽  
Leen A. M. Ali
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Input Image ◽  
Medical Image Segmentation ◽  
Jaccard Index ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

This paper contributes in automating medical image segmentation by proposing generative adversarial network based models to segment both polyps and instruments in endoscopy images. A main contribution of this paper is providing explanations for the predictions using layer-wise relevance propagation approach, showing which pixels in the input image are more relevant to the predictions. The models achieved 0.46 and 0.70, on Jaccard index and 0.84 and 0.96 accuracy, on the polyp segmentation and the instrument segmentation, respectively.

scholarly journals Wildfire-Detection Method Using DenseNet and CycleGAN Data Augmentation-Based Remote Camera Imagery

2020 ◽  
Vol 12 (22) ◽  
pp. 3715 ◽  
Author(s):  
Minsoo Park ◽  
Dai Quoc Tran ◽  
Daekyo Jung ◽  
Seunghee Park
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Model Performance ◽  
Image Data ◽  
Detection Accuracy ◽  
Generative Adversarial Network ◽  
Data Imbalance ◽  
Surveillance Camera ◽  
Convolutional Networks ◽  
Adversarial Network

To minimize the damage caused by wildfires, a deep learning-based wildfire-detection technology that extracts features and patterns from surveillance camera images was developed. However, many studies related to wildfire-image classification based on deep learning have highlighted the problem of data imbalance between wildfire-image data and forest-image data. This data imbalance causes model performance degradation. In this study, wildfire images were generated using a cycle-consistent generative adversarial network (CycleGAN) to eliminate data imbalances. In addition, a densely-connected-convolutional-networks-based (DenseNet-based) framework was proposed and its performance was compared with pre-trained models. While training with a train set containing an image generated by a GAN in the proposed DenseNet-based model, the best performance result value was realized among the models with an accuracy of 98.27% and an F1 score of 98.16, obtained using the test dataset. Finally, this trained model was applied to high-quality drone images of wildfires. The experimental results showed that the proposed framework demonstrated high wildfire-detection accuracy.

Noise Adaptation Generative Adversarial Network for Medical Image Analysis

2020 ◽  
Vol 39 (4) ◽  
pp. 1149-1159 ◽  
Author(s):  
Tianyang Zhang ◽  
Jun Cheng ◽  
Huazhu Fu ◽  
Zaiwang Gu ◽  
Yuting Xiao ◽  
...  
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals When medical images meet generative adversarial network: recent development and research opportunities

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Xiang Li ◽  
Yuchen Jiang ◽  
Juan J. Rodriguez-Andina ◽  
Hao Luo ◽  
Shen Yin ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Real Data ◽  
Image Synthesis ◽  
Future Research ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbstractDeep learning techniques have promoted the rise of artificial intelligence (AI) and performed well in computer vision. Medical image analysis is an important application of deep learning, which is expected to greatly reduce the workload of doctors, contributing to more sustainable health systems. However, most current AI methods for medical image analysis are based on supervised learning, which requires a lot of annotated data. The number of medical images available is usually small and the acquisition of medical image annotations is an expensive process. Generative adversarial network (GAN), an unsupervised method that has become very popular in recent years, can simulate the distribution of real data and reconstruct approximate real data. GAN opens some exciting new ways for medical image generation, expanding the number of medical images available for deep learning methods. Generated data can solve the problem of insufficient data or imbalanced data categories. Adversarial training is another contribution of GAN to medical imaging that has been applied to many tasks, such as classification, segmentation, or detection. This paper investigates the research status of GAN in medical images and analyzes several GAN methods commonly applied in this area. The study addresses GAN application for both medical image synthesis and adversarial learning for other medical image tasks. The open challenges and future research directions are also discussed.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

scholarly journals Restoring Raindrops Using Attentive Generative Adversarial Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 7034
Author(s):  
Hee-Deok Yang
Keyword(s):  
Weather Conditions ◽  
Recurrent Network ◽  
Generative Adversarial Networks ◽  
Navigation Systems ◽  
Vision Systems ◽  
Generative Adversarial Network ◽  
Network Layers ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

scholarly journals Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yingxi Yang ◽  
Hui Wang ◽  
Wen Li ◽  
Xiaobo Wang ◽  
Shizhao Wei ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Sequence Data ◽  
Rapid Development ◽  
Pearson Correlation ◽  
Structural Features ◽  
Generative Adversarial Networks ◽  
Post Translational Modification ◽  
Generative Adversarial Network ◽  
Data Imbalance ◽  
Adversarial Network

Abstract Background Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein’s function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. Method We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. Results In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN. Conclusions The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

scholarly journals Stochastic Restoration of Heavily Compressed Musical Audio Using Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1349
Author(s):  
Stefan Lattner ◽  
Javier Nistal
Keyword(s):  
Data Storage ◽  
Audio Signal ◽  
Human Perception ◽  
Generative Adversarial Networks ◽  
Audio Signals ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Extensive Evaluation ◽  
Listening Tests ◽  
Musical Audio

Lossy audio codecs compress (and decompress) digital audio streams by removing information that tends to be inaudible in human perception. Under high compression rates, such codecs may introduce a variety of impairments in the audio signal. Many works have tackled the problem of audio enhancement and compression artifact removal using deep-learning techniques. However, only a few works tackle the restoration of heavily compressed audio signals in the musical domain. In such a scenario, there is no unique solution for the restoration of the original signal. Therefore, in this study, we test a stochastic generator of a Generative Adversarial Network (GAN) architecture for this task. Such a stochastic generator, conditioned on highly compressed musical audio signals, could one day generate outputs indistinguishable from high-quality releases. Therefore, the present study may yield insights into more efficient musical data storage and transmission. We train stochastic and deterministic generators on MP3-compressed audio signals with 16, 32, and 64 kbit/s. We perform an extensive evaluation of the different experiments utilizing objective metrics and listening tests. We find that the models can improve the quality of the audio signals over the MP3 versions for 16 and 32 kbit/s and that the stochastic generators are capable of generating outputs that are closer to the original signals than those of the deterministic generators.

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