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Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 1283-1307页

作者： Saeed, Ali Q. Abdullah, Siti Norul Huda Sheikh Che-Hamzah, Jemaima Ghani, Ahmad Tarmizi Abdul Abu-ain, Waleed Abdel karim Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur Bangi 43650 Selangor Malaysia Northern Tech Univ Comp Ctr Ninevah 41001 Iraq Univ Kebangsaan Malaysia Fac Med Dept Ophthalmol Kuala Lumpur 43650 Malaysia Taibah Univ Appl Coll Dept Comp Sci Al Madinah Al Munawwara 46429 Saudi Arabia

This study attempts to synthesize a realistic-looking fundus image from a morphologically changed vessel structure using the newly proposed sharpening and varying vessels technique (SVV). This technique sharpens the reconstructed vessels and introduces variation to their structure to generate multiple images from a single input mask. This helps to reduce the reliance on expensive and scarce annotated medical data. The study also aims to overcome the limitations of current methods, such as unrealistic optic disc boundaries, extreme vessel tortuosity, and missed optic discs. This is mainly due to the fact that existing models penalize their weights based on the difference between real and synthetic images using only a single mask. Therefore, their emphasis is on generating the input mask while disregarding other important fundoscopic features. Inspired by the recent progress in Generative Adversarial Nets (GANs) and variational autoencoder (VAE), the proposed approach was able to preserve the geometrical shape of critical fundus characteristics. Visual and quantitative results indicate that the produced images are considerably distinct from the ones used for training. However, they also exhibit anatomical coherence and a reasonable level of visual. The data utilized in this study and the programming code necessary to recreate the experiment can be accessed at https://***/AliSaeed86/SVV_GAN.

关键词： Image synthesis Generative Adversarial Networks Retinal fundus image variational autoencoder Deep learning

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Uncertainty involved drag divergence characteristic predicting method based on VAE

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JOURNAL OF MEMBRANE COMPUTING 2024年第2期6卷 1-14页

作者： Liu, Wei Xie, Hairun Wang, Jing Xiang, Jixin Huang, Zenghui Zhang, Miao Shanghai Aircraft Design & Res Inst 5188 JinKe Rd Shanghai 201210 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut 800 Dongchuan Rd Shanghai 200240 Peoples R China

Effective access to obtain the drag divergence characteristic of an airfoil is crucial for improving the economy, safety, and comfort of the aircraft. Current methods face challenges in providing satisfactory performance concerning efficiency, reliability and generalizability. In this study, an uncertainty-based network is developed, aiming to realize the prediction of the drag characteristic curves and drag divergence characteristics for different supercritical airfoil. To be more specific, a VAE-based network is established, with an encoder to convert the airfoil geometry into low-dimensional latent variables. These latent variables are then simultaneously fed into both the decoder and the drag characteristic prediction network. The decoder is responsible for geometric reconstruction, ensuring that the latent variables maintain a correlation with the geometric features of the airfoil. The prediction network maps the latent variables to the drag characteristic curve. Once trained, the uncertainty distribution of the drag can be realized through sampling from the distribution of latent variables. The analysis of results indicates the model's outstanding performance, with a mean absolute error of 7.407x10-5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.407\times 10<^>{-5}$$\end{document} for the airfoil geometric reconstruction and a mean absolute error of 0.455 count for the drag coefficient prediction. The mean error of the drag divergence Mach number prediction reaches 2.805x10-4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.805\times 10<^>{-4}$$\end{document}. The results show that the model has cons

关键词： Deep learning Supercritical airfoil Drag divergence variational autoencoder

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Experimental Investigation of Spectral Data Enhanced QoT Estimation

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第18期41卷 5885-5894页

作者： Kruse, Lars E. Kuehl, Sebastian Dochhan, Annika Pachnicke, Stephan Univ Kiel Chair Commun D-24143 Kiel Germany

Obtaining telemetry data from the optical signal without demodulation at intermediate nodes of optical networks can be achieved using optical spectrum analyzers. The information gained from the spectrum analysis can be further used for different applications, which include quality of transmission estimation (QoT). Accurate QoT estimation allows to maximize network capacity and minimize margins either through reconfiguration or during the deployment. Analytical solutions for QoT estimation require exact knowledge of the parameters (e.g. fiber lengths, attenuation, non-linearity coefficients), which are not always exactly known in practice especially in multi-vendor networks. Machine learning has shown to be able to handle such parameter-agnostic scenarios. In this paper, we experimentally compare different machine learning based QoT estimators to our developed spectral data driven estimators as well as comparing it to a new approach of using automated feature extraction from the spectrum by a variational autoencoder (VAE). The VAE-based estimation approach is experimentally validated and the required optical spectrum analyzer (OSA) resolutions are investigated. The spectral data driven estimators show to be superior regarding both R-2-score and mean absolute error. Furthermore, the automated feature extraction using the VAE is shown to be a suitable option for accurate optical performance monitoring without demodulation and QoT estimation.

关键词： Estimation Logic gates Signal to noise ratio Optical fiber networks Microprocessors Optical noise Adaptive optics Agnostic networks optical performance monitoring quality of transmission estimation recurrent neural networks variational autoencoder

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UAED: Unsupervised Abnormal Emotion Detection Network Based on Wearable Mobile Device

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 3682-3696页

作者： Zhu, Jiaqi Deng, Fang Zhao, Jiachen Liu, Daoming Chen, Jie Beijing Inst Technol Dept Automat Beijing 100081 Peoples R China Chongqing Innovat Ctr Beijing Inst Technol Chongqing 401120 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200092 Peoples R China Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China

With the development of the internet-of-medical-things, health monitoring through physiological signals has become a critical task. Given this opportunity, research on personal healthcare systems for abnormal emotion detection using physiological signals brings significant benefits to the field of digital healthcare and human-computer interaction. However, it is a challenging task because of the diverse patterns of time series and the lack of labels. In this work, we present a novel model for Unsupervised Abnormal Emotion Detection (UAED) combining Gaussian mixture variational autoencoder (VAE) and convolutional neural networks (CNNs), whose core idea is to reconstruct the input by learning its latent representation thus capturing the normal patterns and applying whitening distance as the anomaly score to detect outliers. In addition, UAED uses stacking operation to transform one-dimensional time series into high-dimension to help the model capture the periodic features and reflect diverse normal patterns. We conduct extensive experiments on four public datasets to demonstrate that our UAED obtains the best performance in various metrics. Furthermore, we deploy UAED in a real environment using a low-cost wearable sensor developed by us to collect electrocardiogram signals and run UAED on mobile terminals with an accuracy of 85%, validating the feasibility of our healthcare system for detecting abnormal emotions.

关键词： Anomaly detection physiological signals variational autoencoder wearable device

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Rigorous Experimental Analysis of Tabular Data Generated using TVAE and CTGAN

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 1250-1262页

作者： Yadav, Parul Gaur, Manish Madhukar, Rahul Kumar Verma, Gaurav Kumar, Pankaj Fatima, Nishat Sarwar, Saqib Dwivedi, Yash Raj Inst Engn & Technol Comp Sci & Engn Dept Lucknow 226021 Uttar Pradesh India

Synthetic data generation research has been progressing at a rapid pace and novel methods are being designed every now and then. Earlier, statistical methods were used to learn the distributions of real data and then sample synthetic data from those distributions. Recent advances in generative models have led to more efficient modeling of complex high-dimensional datasets. Also, privacy concerns have led to the development of robust models with lesser risk of privacy breaches. Firstly, the paper presents a comprehensive survey of existing techniques for tabular data generation and evaluation matrices. Secondly, it elaborates on a comparative analysis of state-of- the-art synthetic data generation techniques, specifically CTGAN and TVAE for small, medium, and large-scale datasets with varying data distributions. It further evaluates the synthetic data using quantitative and qualitative metrics/techniques. Finally, this paper presents the outcomes and also highlights the issues and shortcomings which are still need to be addressed.

关键词： Synthetic data generation tabular data generation data privacy conditional generative adversarial networks variational autoencoder

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A Two-Stage Cascade Model with variational autoencoders and Attention Gates for MRI Brain Tumor Segmentation 6th

A Two-Stage Cascade Model with Variational Autoencoders and ...

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6th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Lyu, Chenggang Shu, Hai NYU Dept Biostat Sch Global Publ Hlth New York NY 10003 USA

ISBN: (纸本)9783030720834;9783030720841

Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953, 6.299, 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://***/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.

关键词： Attention gate Brain tumor segmentation Encoder-decoder network variational autoencoder

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SELF-ORGANIZED variational autoencoderS (SELF-VAE) FOR LEARNED IMAGE COMPRESSION

SELF-ORGANIZED VARIATIONAL AUTOENCODERS (SELF-VAE) FOR LEARN...

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IEEE International Conference on Image Processing (ICIP)

作者： Yilmaz, M. Akin Keles, Onur Guven, Hilal Tekalp, A. Murat Malik, Junaid Kiranyaz, Serkan Koc Univ Dept Elect & Elect Engn TR-34450 Istanbul Turkey Tampere Univ Tampere Finland Qatar Univ Doha Qatar

ISBN: (纸本)9781665441155

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their "self-organized" variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity. The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality.

关键词： end-to-end learned image compression variational autoencoder self-organized operational layer rate-distortion performance perceptual quality metrics

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Modality Completion via Gaussian Process Prior variational autoencoders for Multi-modal Glioma Segmentation 24th

Modality Completion via Gaussian Process Prior Variational A...

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International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Hamghalam, Mohammad Frangi, Alejandro F. Lei, Baiying Simpson, Amber L. Queens Univ Sch Comp Kingston ON Canada Islamic Azad Univ Fac Elect Qazvin Branch Qazvin Iran Queens Univ Dept Biomed & Mol Sci Kingston ON Canada Univ Leeds CISTIB Ctr Computat Imaging & Simulat Technol Bio Sch Comp Leeds LS2 9LU W Yorkshire England LICAMM Leeds Inst Cardiovasc & Metab Med Sch Med Leeds LS2 9LU W Yorkshire England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Univ Hosp Gasthuisberg Herestr 49 B-3000 Leuven Belgium Shenzhen Univ Hlth Sci Ctr Sch Biomed EngnGuangdong Key Lab Biomed Measurem Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China

ISBN: (纸本)9783030872342;9783030872335

In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e.g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations. In some cases, certain protocols are unavailable due to limited scan time or to retrospectively harmonise the imaging protocols of two independent studies. Missing image modalities pose a challenge to segmentation frameworks as complementary information contributed by the missing scans is then lost. In this paper, we propose a novel model, Multi-modal Gaussian Process Prior variational autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the variational autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. Instead of designing one network for each possible subset of present sub-modalities or using frameworks to mix feature maps, missing data can be generated from a single model based on all the available samples. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing. Our experiments against competitive segmentation baselines with missing sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE model for segmentation tasks.

关键词： Missing modality Gaussian process variational autoencoder Glioma segmentation MRI

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An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

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Financial Innovation 2021年第1期7卷 585-608页

作者： Hakan Gunduz Software Engineering Department Bandirma Onyedi Eylul University10200 BalikesirTurkey

In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)*** models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure *** the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through variational autoencoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our *** combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature *** the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of *** the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_*** all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock *** was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

关键词： Stock market prediction variational autoencoder Recursive feature elimination Long-short term memory Borsa Istanbul LightGBM

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Detection and classification of electrocardiography using hybrid deep learning models

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HELLENIC JOURNAL OF CARDIOLOGY 2025年 81卷 75-84页

作者： Selvam, Immaculate Joy Madhavan, Moorthi Kumarasamy, Senthil Kumar Saveetha Engn Coll Dept Elect & Commun Engn Chennai 602105 India Saveetha Engn Coll Dept Biomed Engn Chennai 602105 India Cent Polytech Coll Dept Elect & Commun Engn Chennai 600113 India

OBJECTIVE Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features. METHODS Precision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and variational autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over the PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 s. The classification evaluation of five super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared. RESULTS The classification of various CVDs resulted in the highest accuracy of 98.51%, specificity of 98.12%, sensitivity of 97.9%, and F1-score of 97.95%. We have also achieved the minimum false positive and false negative rates of 2.07% and 1.87%, respectively, during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record. CONCLUSION When compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in the testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help clinicians to identify the disease earlier and carry out further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture. (Hellenic Journal of Cardiology 2025;81:75-84) (c) 2024 Hellenic Society of Cardiology. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Electrocardiography Cardiovascular diseases Convolutional neural network variational autoencoder PTB-XL database

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