Deep Generative Adversarial Networks Research Articles

Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks

The effectiveness of detecting malicious files heavily relies on the quality of the training dataset, particularly its size and authenticity. However, the lack of high-quality training data remains one of the biggest challenges in achieving widespread adoption of malware detection by trained machine and deep learning models. In response to this challenge, researchers have made initial strides by employing generative techniques to create synthetic malware samples. This work utilizes deep variational autoencoders (VAE) and generative adversarial networks (GAN) to produce malware samples as opcode sequences. The generated malware opcodes are then distinguished from authentic opcode samples using machine and deep learning techniques as validation methods. The primary objective of this study was to compare synthetic malware generated using VAE and GAN technologies. The results showed that neither approach could create synthetic malware that could deceive machine learning classification. However, the WGAN-GP algorithm showed more promise by requiring a higher number of synthetic malware samples in the train set to effectively be detected, proving ita better approach in synthetic malware generation.

Reinforced black widow algorithm with restoration technique based on optimized deep generative adversarial network

ABSTRACT Image restoration is used to develop the quality of image that is triggered by various noises and blurring. During this causes, certain areas in the images are vanished. The existing works does not provide sufficient restoration process with high accuracy. Therefore, a new image restoration system based on Optimized Deep Generative Adversarial Network (DGAN) with Reinforced Black Widow algorithm (BWOA) is proposed in this paper to increase the restoration accuracy and reducing the noises. At first, the input image is converted as gray scale image and the multi-scale edge information is removed as damaged area of an image by constructing a smooth function. Here, the extracted multi-scale edge information is given to the DGAN model. After that, the images are trained to create the best fake images through continuous play among generator and discriminator. Then, the detected images are restored in the original image with high accuracy. The hyper parameters of the DGAN are optimized by using the BWOA. The major objective of this paper is ‘to increase the restoration accuracy and the quality of the image by decreasing the noises occurred in the input image.’ The simulation process is performed on the MATLAB platform. The proposed DGAN-BWOA-IR attains higher restoration accuracy of 9.3%, higher PSNR value 74.589(db), SSIM 9.023% the proposed system is likened with the existing approaches, such as plug-and-play image restoration including deep denoiser prior (DCNN-IR), Learning enriched features for rapid image restoration with enhancement (LEF-IR), Exploiting deep generative prior for versatile image restoration with manipulation (GAN-IR), respectively.

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals. In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images. Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, the zigzag generative adversarial network (ZZ-GAN) is used as the learning network to generate the secret key. The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operation in both encryption and decryption using the proposed ZZ-GAN. Encrypting the original image requires a secret key generated during encryption. After identification, the encrypted image is decrypted using the generated key to reverse the encryption process. Finally, speckle noise is removed from the encrypted image in order to reconstruct the original image. According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images.

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

Distance Regression Enhanced with Temporal Information Fusion and Adversarial Training for Robot-Assisted Endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) has a role in characterising tissue intraoperatively to guide tumour resection during surgery. To capture good quality pCLE data which is important for diagnosis, the probe-tissue contact needs to be maintained within a working range of micrometre scale. This can be achieved through micro-surgical robotic manipulation which requires the automatic estimation of the probe-tissue distance. In this paper, we propose a novel deep regression framework composed of the Deep Regression Generative Adversarial Network (DR-GAN) and a Sequence Attention (SA) module. The aim of DR-GAN is to train the network using an enhanced image-based supervision approach. It extents the standard generator by using a well-defined function for image generation, instead of a learnable decoder. Also, DR-GAN uses a novel learnable neural perceptual loss which combines for the first time spatial and frequency domain features. This effectively suppresses the adverse effects of noise in the pCLE data. To incorporate temporal information, we've designed the SA module which is a cross-attention module, enhanced with Radial Basis Function based encoding (SA-RBF). Furthermore, to train the regression framework, we designed a multi-step training mechanism. During inference, the trained network is used to generate data representations which are fused along time in the SA-RBF module to boost the regression stability. Our proposed network advances SOTA networks by addressing the challenge of excessive noise in the pCLE data and enhancing regression stability. It outperforms SOTA networks applied on the pCLE Regression dataset (PRD) in terms of accuracy, data quality and stability.

A deep learning approach for prediction of air quality index in smart city

Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model.

Compressed Video Sensing Based on Deep Generative Adversarial Network

Compressed Video Sensing Based on Deep Generative Adversarial Network

Autism Spectrum Disorder Detection Using Parallel Deep Convolution Neural Network and Generative Adversarial Networks

Autism Spectrum Disorder Detection Using Parallel Deep Convolution Neural Network and Generative Adversarial Networks

Wall-Cor Net: wall color replacement via Clifford chance-based deep generative adversarial network

Wall-Cor Net: wall color replacement via Clifford chance-based deep generative adversarial network

506 Deep Learning-Based Image-to-Image Translation to Identify Macrophage Infiltration in High-Grade Glioma Using Label-Free Stimulated Raman Histology

INTRODUCTION: 5-Aminolevulinic acid (5-ALA) is the most widely used fluorophore in image-guided glioma surgery, and previous work at our institution demonstrated that 5-ALA highlights tumor-associated macrophages (TAMs) in two-photon microscopy images of brain tumor tissue. Using a unique, paired dataset of stimulated Raman histology (SRH) and two-photon images that share one-to-one spatial resolution, we propose a deep-learning approach to identify macrophages from intraoperative SRH images without requiring fluorescent labels. METHODS: We compiled a dataset of 9,554 non-overlapping, 300-by-300 pixel fields of view from paired SRH and two-photon images representing 40 cases of high-grade glioma. A deep generative adversarial network (pix2pix), consisting of a U-Net generator and PatchGAN discriminator, was trained to generate synthetic two-photon images from each SRH patch. The model was trained for 200 epochs, and similarity between the synthetic and real distributions was assessed qualitatively while training and quantitatively using FrÉchet inception distance (FID). RESULTS: Our model was successfully trained to generate synthetic two-photon images nearly indistinguishable from ground truth examples. The FID between a held-out test set of real and synthetic images was 8.58, compared to a mean FID of 9.26 ± 0.24 between randomly sampled sets of real images. Our model consistently inferred the location of brightly fluorescing TAMs in held-out, previously unseen test images. CONCLUSIONS: We used deep learning to visualize TAMs in label-free SRH images. Analysis of TAM infiltration has the potential to identify patients most likely to benefit from immunotherapy clinical trials. Ongoing work will leverage additional deep-learning approaches to automatically identify and quantify TAM infiltration with SRH, enabling rapid patient-specific analysis of the glioma immune microenvironment.

Generation and applications of synthetic computed tomography images for neurosurgical planning.

CT and MRI are synergistic in the information provided for neurosurgical planning. While obtaining both types of images lends unique data from each, doing so adds to cost and exposes patients to additional ionizing radiation after MRI has been performed. Cross-modal synthesis of high-resolution CT images from MRI sequences offers an appealing solution. The authors therefore sought to develop a deep learning conditional generative adversarial network (cGAN) which performs this synthesis. Preoperative paired CT and contrast-enhanced MR images were collected for patients with meningioma, pituitary tumor, vestibular schwannoma, and cerebrovascular disease. CT and MR images were denoised, field corrected, and coregistered. MR images were fed to a cGAN that exported a "synthetic" CT scan. The accuracy of synthetic CT images was assessed objectively using the quantitative similarity metrics as well as by clinical features such as sella and internal auditory canal (IAC) dimensions and mastoid/clinoid/sphenoid aeration. A total of 92,981 paired CT/MR images obtained in 80 patients were used for training/testing, and 10,068 paired images from 10 patients were used for external validation. Synthetic CT images reconstructed the bony skull base and convexity with relatively high accuracy. Measurements of the sella and IAC showed a median relative error between synthetic CT scans and ground truth images of 6%, with greater variability in IAC reconstruction compared with the sella. Aerations in the mastoid, clinoid, and sphenoid regions were generally captured, although there was heterogeneity in finer air cell septations. Performance varied based on pathology studied, with the highest limitation observed in evaluating meningiomas with intratumoral calcifications or calvarial invasion. The generation of high-resolution CT scans from MR images through cGAN offers promise for a wide range of applications in cranial and spinal neurosurgery, especially as an adjunct for preoperative evaluation. Optimizing cGAN performance on specific anatomical regions may increase its clinical viability.

Deep Generative Models for Fast Photon Shower Simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Abstract B023: Modeling of new drugs clinical trials outcome with patients’ digital twins cohorts

Abstract There is a problem of clinical trial failure, as each new drug should surpass the effectiveness of existing treatment regimens, which becomes increasingly challenging over time. Another significant issue is treating patients who have developed resistance to the current therapies. Essentially, the use of drug combinations or off-label drug use, where the indication does not match the diagnosis, is akin to an experiment, as there is insufficient data on which drug or combination to use. This work proposes an approach utilizing computer modeling of patients using gene expression and clinical data. Deep learning and generative adversarial networks are employed as modeling tools. The training data for the algorithms were sourced from publicly available databases such as TCGA, Drugbank, CCLE, and GDSC. The modeling is based on the hypothesis of similarity between patients, similarity between drugs, as well as the similarity between individual organs and patient tissues with cell lines, with similarity being computed mathematically. As a result, a patient model is created, where the input consists of drugs and their combinations, and the output provides survival probability values. These model data can be generated in any required quantity with generative adversarial networks (GAN) technology to create observation and control groups. Consequently, it becomes possible to simulate clinical trials, forecasting their outcomes, and, most importantly, optimizing the trial parameters to maximize the likelihood of success. We obtained patient data and drug information from the TCGA (The Cancer Genome Atlas) database. Biologicals were excluded, and only small molecules, including targeted therapies and chemotherapeutic agents, were retained. The dataset consisted of information on 3225 patients and the effects of 161 drugs. According to our hypothesis, the patient's profile includes three groups of factors, and each of these groups contributes approximately equally to the duration of overall survival (OS) and disease-free survival (DFS) intervals. The first group comprises clinical data such as age, diagnosis, and disease stage. The second group consists of genetic data, specifically gene expression profiles, which capture the functional characteristics of genes. The third group represents the drug or combination of drugs used for patient treatment. Therefore, it is necessary to ensure that all three groups of factors have comparable dimensions of influence. For drug data, we obtained information from the DrugBank database. We represented the drug molecules using SMILES (Simplified Molecular Input Line Entry System) notation, which were then converted into 100-dimensional vectors using embedding techniques derived from natural language processing technologies. To represent combinations of drugs, we employed vector addition of the individual drug vectors. As the outcome measure for prediction, we considered numerical values representing the durations of OS and DFS intervals for each respective task. Citation Format: Dmitrii K. Chebanov, Vsevolod A. Misyurin. Modeling of new drugs clinical trials outcome with patients’ digital twins cohorts [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B023.

SSDNet: A SEMISUPERVISED DEEP GENERATIVE ADVERSARIAL NETWORK FOR ELECTROENCEPHALOGRAM-BASED EMOTION RECOGNITION

This research introduces a novel method for emotion recognition using Electroencephalography (EEG) signals, leveraging advancements in emotion computing and EEG signal processing. The proposed method utilizes a semisupervised deep convolutional generative adversarial network (SSDNet) as the central model. The model fully integrates the feature extraction methods of the generative adversarial network (GAN), deep convolutional GAN (DCGAN), spectrally normalized GAN (SSGAN) and the encoder, maximizing the advantages of each method to construct a more accurate emotion classification model. In our study, we introduce a flow-form-consistent merging pattern, which successfully addresses mismatches between the data by fusing the EEG data with the features. Implementing this merging pattern not only enhances the uniformity of the input but also decreases the computational load on the network, resulting in a more efficient model. By conducting experiments on the DEAP and SEED datasets, we evaluate the SSDNet model proposed in this paper in detail. The experimental results show that the accuracy of the proposed algorithm is improved by 6.4% and 8.3% on the DEAP and SEED datasets, respectively, compared to the traditional GAN. This significant improvement in performance validates the effectiveness and feasibility of the SSDNet model. The research contributions of this paper are threefold. First, we propose and implement an SSDNet model that integrates multiple feature extraction methods, providing a more accurate and comprehensive solution for emotion recognition tasks. Second, by introducing a flow-form-consistent merging pattern, we successfully address the problem of interdata mismatches and improve the generalization performance of the model. Finally, we experimentally demonstrate that the method in this paper achieves a significant improvement in accuracy over the traditional GANs on the DEAP and SEED datasets, providing an innovative solution in the field of EEG-based emotion recognition.

A hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization.

Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors. This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting. The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification. A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew's correlation coefficient. It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory.

Integrated Generative Adversarial Networks and Deep Convolutional Neural Networks for Image Data Classification: A Case Study for COVID-19

Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model’s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score.

BCC and Immunocryosurgery scar differentiation through computational resolution-enhanced OCT images and skin optical attenuation: A proof-of-concept study.

Monitoring medical therapy remains a challenging task across all non-surgical skin cancer treatment modalities. In addition, confirmation of residual tumours after treatment is essential for the early detection of potential relapses. Optical coherence tomography (OCT), a non-invasive method for real-time cross-sectional imaging of living tissue, is a promising imaging approach for assessing relatively flat, near-surface skin lesions, such as those that occur in most basal cell carcinomas (BCCs), at the time of diagnosis. However, the skin's inherent property of strong light scattering impedes the implementation of OCT in these cases due to the poor image quality. Furthermore, translating OCT's optical parameters into practical use in routine clinical settings is complicated due to substantial observer subjectivity. In this retrospective pilot study, we developed a workflow based on the upscale of the OCT images resolution using a deep generative adversarial network and the estimation of the skin optical attenuation coefficient. At the site of immunocryosurgery-treated BCC, the proposed methodology can extract optical parameters and discriminate objectively between tumour foci and scar tissue.

Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems

Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems

Revolutionizing Magnetic Resonance Imaging Image Reconstruction: A Unified Approach Integrating Deep Residual Networks and Generative Adversarial Networks

Revolutionizing Magnetic Resonance Imaging Image Reconstruction: A Unified Approach Integrating Deep Residual Networks and Generative Adversarial Networks