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Detection and localization of myocardial infarction based on a convolutional autoencoder

KNOWLEDGE-BASED SYSTEMS 2019年第0期178卷 123-131页

作者： Sugimoto, Kaiji Kon, Yudai Lee, Saerom Okada, Yoshifumi Muroran Inst Technol Div Informat & Elect Engn 27-1 Mizumoto Cho Muroran Hokkaido 0508585 Japan Muroran Inst Technol Dept Informat & Elect Engn 27-1 Mizumoto Cho Muroran Hokkaido 0508585 Japan Muroran Inst Technol Coll Informat & Syst 27-1 Mizumoto Cho Muroran Hokkaido 0508585 Japan

Twelve-lead electrocardiograms (ECG) are widely used for the diagnosis of myocardial infarction (MI). For MI detection and localization, 12 ECG signals should be comprehensively checked through visual observation. This process is time-consuming, requires significant effort, and is prone to inducing errors. Hence, computer-aided automatic detection technology is required. Many existing methods perform MI detection and localization using features extracted from normal and abnormal ECG data. However, abnormal ECG signals show various waveforms for the same heart disease;therefore, it is difficult to extract the waveform features common to all the waveforms. In addition, ECG data is extremely imbalanced, and the minority class, including abnormal ECG data, may not be adequately learned. Because of the difficulty of feature extraction in the imbalanced data, in this study, we propose a new method for MI detection and localization that learns only normal ECG data in the public ECG database. This method is based on a convolutional autoencoder (CAE) model for normal ECG waveforms. The CAE model is constructed for each lead and outputs reconstructed input ECG data if normal ECG data is inputted. Otherwise, the waveform is distorted and outputted. MI detection and localization is performed by a k-nearest neighbor (k-NN) classifier using an error vector whose dimension corresponds to each lead and whose element is a degree of deviation between the normal ECG data and the output waveform. In the experiments, the classification performance of the proposed method was evaluated using 353640 beats obtained from the ECG data of MI patients (10 class infarct sites) and healthy subjects. Consequently, the proposed scheme demonstrated a classification performance higher than or comparable to that of existing methods, and the false positive and false negative rates could be reduced compared to existing methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Electrocardiogram Myocardial infarction convolutional autoencoder k-nearest neighbor

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Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully convolutional autoencoder

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IEEE ACCESS 2019年 7卷 36433-36445页

作者： Li, Xingyu Radulovic, Marko Kanjer, Ksenija Plataniotis, Konstantinos N. Univ Toronto Edward S Rogers Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Inst Oncol & Radiol Serbia Dept Expt Oncol Natl Canc Res Ctr Belgrade 11000 Serbia

Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in a weak-supervised manner and generate a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and one-layer neural network. We apply the proposed method to a public breast cancer image set. Our results, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.

关键词： Breast cancer diagnosis abnormality detection convolutional autoencoder discriminative pattern learning histopathology image analysis

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Anomaly detection of defects on concrete structures with the convolutional autoencoder

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ADVANCED ENGINEERING INFORMATICS 2020年 45卷 101105-101105页

作者： Chow, J. K. Su, Z. Wu, J. Tan, P. S. Mao, X. Wang, Y. H. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

This paper reports the application of deep learning for implementing the anomaly detection of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure. A convolutional autoencoder was trained as a reconstruction-based model, with the defect-free images, to rapidly and reliably detect defects from the large volume of image datasets. This training process was in the unsupervised mode, with no label needed, thereby requiring no prior knowledge and saving an enormous amount of time for label preparation. The built anomaly detector favors minimizing the reconstruction errors of defect-free images, which renders high reconstruction errors of defects, in turn, detecting the location of defects. The assessment shows that the proposed anomaly detection technique is robust and adaptable to defects on wide ranges of scales. Comparison was also made with the segmentation results produced by other automatic classical methods, revealing that the results made by the anomaly map outperform other segmentation methods, in terms of precision, recall, F-1 measure and F-2 measure, without severe under- and over-segmentation. Further, instead of merely being a binary map, each pixel of the anomaly map is represented by the anomaly score, which acts as a risk indicator for alerting inspectors, wherever defects on concrete structures are detected.

关键词： Anomaly detection Unsupervised learning convolutional autoencoder Concrete structure Cracking Spalling

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Domain Adaptation with Stacked convolutional Sparse autoencoder 28th

Domain Adaptation with Stacked Convolutional Sparse Autoenco...

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28th International Conference on Neural Information Processing

作者： Zhu, Yi Zhou, Xinke Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China

ISBN: (纸本)9783030923068;9783030923075

Domain adaptation aims to transfer knowledge from source domain to promote the learning task in target domain. The key problem of domain adaptation is how to learn more abstract and discriminative features for across domains. Recently, autoencoder based methods have achieved substantial performance in domain adaptation for the advantages of no label requirement and fast convergence speed. However, most of existing methods rely on the classical structure of autoencoder or integrate the regularization item into the objective function, which poses challenges for more abstract feature representation learning. To address this problem, we propose a domain adaptation method with Stacked convolutional Sparse autoencoder (short for SCSA). More specifically, more discriminative features are learned by convolutional lower layers with kernels learned by a sparse autoencoder. Meanwhile, the Reconstruction Independent Component Analysis (RICA) is introduced for independent component analysis of the input data. Extensive experimental results demonstrate the effectiveness of our proposed methods compared to several state-of-the-art domain adaptation methods.

关键词： Domain adaptation Representation learning convolutional autoencoder Sparse autoencoder

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A Feature Extraction Method Based on convolutional autoencoder for Plant Leaves Classification

A Feature Extraction Method Based on Convolutional Autoencod...

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2nd IEEE Colombian Conference on Computational Intelligence (ColCACI)

作者： Paco Ramos, Mery Paco Ramos, Vanessa Loaiza Fabian, Arnold Osco Mamani, Erbert Univ Nacl Jorge Basadre Grohmann Tacna Peru

ISBN: (纸本)9781728116143

In this research, we present an approach based on convolutional autoencoder (CAE) and Support Vector Machine (SVM) for leaves classification of different trees. While previous approaches relied on image processing and manual feature extraction, the proposed approach operates directly on the image pixels, without any preprocessing. Firstly, we use multiple layers of CAE to learn the features of leaf image dataset. Secondly, the extracted features were used to train a linear classifier based on SVM. Experimental results show that the classifiers using these features can improve their predictive value, reaching an accuracy rate of 94.74 %.

关键词： Feature Extraction Image Processing Plant Classification convolutional autoencoder Deep Learning Computer Vision

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DENOISING convolutional autoencoder BASED B-MODE ULTRASOUND TONGUE IMAGE FEATURE EXTRACTION 44

DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND ...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing Univ Posts & Telecommun Automat Dept Beijing 100876 Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China Univ Michigan Dept Linguist Ann Arbor MI 48109 USA

ISBN: (纸本)9781479981311

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1.

关键词： B-mode ultrasound tongue imaging unsupervised learning feature extraction convolutional autoencoder denoising convolutional autoencoder

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A Feature Extraction Method Based on convolutional autoencoder for Plant Leaves Classification 2nd

A Feature Extraction Method Based on Convolutional Autoencod...

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2nd IEEE Colombian Conference on Computational Intelligence (ColCACI)

作者： Paco Ramos, Mery M. Paco Ramos, Vanessa M. Loaiza Fabian, Arnold Osco Mamani, Erbert F. Univ Nacl Jorge Basadre Grohmann Tacna Peru

ISBN: (纸本)9783030362119;9783030362102

关键词： Feature extraction Image processing Plant classification convolutional autoencoder Deep learning Computer vision

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Anomaly Detection for Industrial Control Systems Using K-Means and convolutional autoencoder 27

Anomaly Detection for Industrial Control Systems Using K-Mea...

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27th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Chang, Chun-Pi Hsu, Wen-Chiao Liao, I-En Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan Natl Taichung Univ Sci & Technol Dept Informat Management Taichung Taiwan

ISBN: (纸本)9789532900880

With the rise of Internet of Things technology and smart manufacturing, the issues of cyber security in industrial control systems (ICS) have become of great concern. Critical infrastructures, such as power plants and water treatment facilities, which heavily rely on ICS, have increasingly come under cyberattack in recent years. These cyberattacks pose severe threats to public safety as well as national security. Therefore, protecting ICS from cyberattack is an important research issue in the era of information technology and operation technology convergence. In this study, we propose an anomaly detection method based on semi-supervised techniques for industrial control systems, using the k-means and convolutional autoencoder methods. Experimental results show that the proposed method outperforms the other methods in almost all performance metrics for gas pipeline and water storage tank datasets.

关键词： anomaly detection intrusion detection system industrial control system k-means convolutional autoencoder

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Power Doppler Imaging with a Novel convolutional autoencoder Based Clutter Filtering Technique: Feasibility Study

Power Doppler Imaging with a Novel Convolutional Autoencoder...

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IEEE International Ultrasonics Symposium (IUS)

作者： Shih, Chih-Hsiang Li, Meng-Lin Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan Natl Tsing Hua Univ Inst Photon Technol Hsinchu Taiwan

ISBN: (纸本)9781728145969

Singular value decomposition (SVD)-based clutter filters have shown its outstanding ability in clutter rejection for ultrafast plane-wave Doppler imaging. However, automatic decision on order selection still remains a big problem in vivo and the computational complexity of the SVD-based clutter filter is also high for clinical translation. In this study, we propose a novel convolutional autoencoder based clutter filtering technique for power Doppler, which retains the characteristic principle component analysis (PCA) done by the SVD filtering while being with no need for order selection and potentially lower computational load. The autoencoder is a counterpart for PCA in deep learning field. The simulation results show that the proposed convolutional autoencoder clutter filtering technique can robustly reject tissue clutter whose spectrum overlaps that of the blood signal, which is impossible to accomplish with conventional highpass filtering, and outperforms the SVD filtering. Our simulation results show that the contrast-to-noise ratio (i.e., blood-to-clutter ratio) of our proposed method is better than those of the SVD-based filter and the highpass filter by 23.97 dB and 64.17 dB, respectively.

关键词： power Doppler clutter filter singular value decomposition convolutional autoencoder neural network

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Semi-supervised Learning for Epileptic Focus Localization Using Deep convolutional autoencoder

Semi-supervised Learning for Epileptic Focus Localization Us...

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IEEE Biomedical Circuits and Systems Conference (BioCAS)

作者： Daoud, Hisham Bayoumi, Magdy Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

ISBN: (纸本)9781509006175

Epileptic focus localization is an important process for successful epileptic surgery. The intracranial Electroencephalogram (iEEG) recording helps the neurosurgeons to take the right decision in patients undergoing epilepsy surgical procedure by delineating the epileptogenic area. This requires accurate classification of the EEG signals from different brain areas into focal and non-focal groups. In this paper, we propose a semi-supervised learning based system utilizing a deep neural network to accurately analyze the non-stationary and nonlinear EEG signals. The presented method is based on training a deep convolutional autoencoder neural network in an unsupervised manner and then using the pre-trained encoder along with multi-layer perceptron for classification of the EEG signals into focal and non-focal. EEG feature extraction and classification are performed in a single automated system rather than extracting handcrafted features as in the previous work. The proposed approach leverages the convolutional autoencoder by reducing the features' dimension and extracting the discriminative spatio-temporal features from the EEG signals. Experimental results show the ability of the proposed method to localize the epileptogenic area with an average accuracy of 93.2% using ten-fold cross validation strategy which is the highest among the state of the art.

关键词： EEG epileptic focus localization classification convolutional autoencoder

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