AWGAN: A Powerful Batch Correction Model for scRNA-seq Data

With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However, integrative downstream analysis from multiple scRNA-seq datasets remains challenging due to batch effects. In this paper, we focus on scRNA-seq data integration and propose a new deep learning framework based on Wasserstein Generative Adversarial Network (WGAN) combined with an attention mechanism to reduce the differences among batches. We also discuss the limitations of the existing methods and demonstrate the advantages of our new model from both theoretical and practical aspects, advocating the use of deep learning in genomics research.

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deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

Frontiers in Genetics ◽

10.3389/fgene.2021.708981 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bin Zou ◽

Tongda Zhang ◽

Ruilong Zhou ◽

Xiaosen Jiang ◽

Huanming Yang ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Batch Correction ◽

Single Cell Rna Sequencing ◽

Identical Cell

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

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Coverless Image Steganography Based on Generative Adversarial Network

Mathematics ◽

10.3390/math8091394 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1394 ◽

Cited By ~ 2

Author(s):

Jiaohua Qin ◽

Jing Wang ◽

Yun Tan ◽

Huajun Huang ◽

Xuyu Xiang ◽

...

Keyword(s):

Deep Learning ◽

Poor Quality ◽

Image Steganography ◽

Cover Image ◽

Secret Message ◽

Generative Adversarial Network ◽

Learning Framework ◽

Adversarial Network ◽

Traditional Image

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

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Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100389 ◽

2018 ◽

Vol 7 (10) ◽

pp. 389 ◽

Cited By ~ 19

Author(s):

Wei He ◽

Naoto Yokoya

Keyword(s):

Deep Learning ◽

Large Scale ◽

Super Resolution ◽

Optical Image ◽

Optical Data ◽

Image Simulation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Multi Temporal ◽

Sar Data

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.

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A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure

Nature Communications ◽

10.1038/s41467-021-26703-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zhijian Yang ◽

Ilya M. Nasrallah ◽

Haochang Shou ◽

Junhao Wen ◽

Jimit Doshi ◽

...

Keyword(s):

Deep Learning ◽

Brain Structure ◽

Brain Diseases ◽

Normal Brain ◽

Generative Adversarial Network ◽

Cognitively Normal ◽

Learning Framework ◽

Adversarial Network ◽

Normal Individuals ◽

Disease Subtypes

AbstractHeterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.

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MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain

AI ◽

10.3390/ai2040036 ◽

2021 ◽

Vol 2 (4) ◽

pp. 600-620

Author(s):

Gabriele Accarino ◽

Marco Chiarelli ◽

Francesco Immorlano ◽

Valeria Aloisi ◽

Andrea Gatto ◽

...

Keyword(s):

Deep Learning ◽

Statistical Downscaling ◽

Large Scale ◽

Super Resolution ◽

Climate Science ◽

Cost Effective ◽

Temperature Fields ◽

Generative Adversarial Network ◽

Multi Scale ◽

Adversarial Network

One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide new insights and modeling solutions to tackle downscaling-related tasks by automatically learning the coarse-to-fine grained resolution mapping. In particular, deep learning models designed for super-resolution problems in computer vision can be exploited because of the similarity between images and climatic fields maps. For this reason, a new architecture tailored for statistical downscaling (SD), named MSG-GAN-SD, has been developed, allowing interpretability and good stability during training, due to multi-scale gradient information. The proposed architecture, based on a Generative Adversarial Network (GAN), was applied to downscale ERA-Interim 2-m temperature fields, from 83.25 to 13.87 km resolution, covering the EURO-CORDEX domain within the 1979–2018 period. The training process involves seasonal and monthly dataset arrangements, in addition to different training strategies, leading to several models. Furthermore, a model selection framework is introduced in order to mathematically select the best models during the training. The selected models were then tested on the 2015–2018 period using several metrics to identify the best training strategy and dataset arrangement, which finally produced several evaluation maps. This work is the first attempt to use the MSG-GAN architecture for statistical downscaling. The achieved results demonstrate that the models trained on seasonal datasets performed better than those trained on monthly datasets. This study presents an accurate and cost-effective solution that is able to perform downscaling of 2 m temperature climatic maps.

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Outlier Detection in IoT Using Generative Adversarial Network

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206452 ◽

2020 ◽

pp. 306-311

Author(s):

B. Joyce Beula Rani ◽

L. Sumathi

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Outlier Detection ◽

Large Scale ◽

Detection Methods ◽

Learning Approach ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Detection Approach ◽

Iot Devices

Usage of IoT products have been rapidly increased in past few years. The large number of insecure Internet of Things (IoT) devices with low computation power makes them an easy and attractive target for attackers seeking to compromise these devices and use them to create large-scale attacks. Detecting those attacks is a time consuming task and it needs to be identified shortly since it keeps on spreading. Various detection methods are used for detecting these attacks but attack mechanism keeps on evolving so a new detection approach must be introduced to detect their presence and thus blocking their spreading. In this paper a deep learning approach called GAN – Generative Adversarial Network can be used to detect this outlier and achieve 85% accuracy.

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A new method to predict anomaly in brain network based on graph deep learning

Reviews in the Neurosciences ◽

10.1515/revneuro-2019-0108 ◽

2020 ◽

Vol 31 (6) ◽

pp. 681-689

Author(s):

Jalal Mirakhorli ◽

Hamidreza Amindavar ◽

Mojgan Mirakhorli

Keyword(s):

Deep Learning ◽

Large Scale ◽

Brain Plasticity ◽

Brain Network ◽

High Order ◽

Brain Diseases ◽

Simultaneous Occurrence ◽

Generative Adversarial Network ◽

The Brain ◽

Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

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An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽

10.3390/info12060249 ◽

2021 ◽

Vol 12 (6) ◽

pp. 249

Author(s):

Xin Jin ◽

Yuanwen Zou ◽

Zhongbing Huang

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Image Classification ◽

Classification Accuracy ◽

Data Augmentation ◽

Cycle Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

10.30632/spwla-2021-0081 ◽

2021 ◽

Author(s):

James Howard ◽

◽

Joe Tracey ◽

Mike Shen ◽

Shawn Zhang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Nearest Neighbor ◽

Rock Fracture ◽

Short Interval ◽

Acoustic Properties ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Deep Learning Neural Network ◽

Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

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Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽

10.3390/s18113913 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3913 ◽

Cited By ~ 6

Author(s):

Mingxuan Li ◽

Ou Li ◽

Guangyi Liu ◽

Ce Zhang

Keyword(s):

Deep Learning ◽

Cognitive Radio ◽

Supervised Learning ◽

Rapid Development ◽

Generative Adversarial Networks ◽

Modulation Recognition ◽

Learning Methods ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

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