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A Two Layer sparse autoencoder for Glaucoma Identification with Fundus Images

JOURNAL OF MEDICAL SYSTEMS 2019年第9期43卷 1-9页

作者： Raghavendra, U. Gudigar, Anjan Bhandary, Sulatha V. Rao, Tejaswi N. Ciaccio, Edward J. Acharya, U. Rajendra Manipal Acad Higher Educ Manipal Inst Technol Dept Instrumentat & Control Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Kasturba Med Coll Dept Ophthalmol Manipal 576104 Karnataka India Columbia Univ Dept Med New York NY USA Ngee Ann Polytech Dept Elect & Comp Engn Clementi 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Clementi 599491 Singapore Taylors Univ Fac Hlth & Med Sci Sch Med Subang Jaya Malaysia

Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F-measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.

关键词： CAD Cascade Glaucoma sparse autoencoder

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Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2018年第3期38卷 733-744页

作者： Abraham, Bejoy Nair, Madhu S. Univ Kerala Dept Comp Sci Thiruvananthapuram 695581 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi 682022 Kerala India

A novel method to diagnose clinically significant prostate cancer (PCa) using Multi-parametric Magnetic Resonance Imaging (mpMRI) biomarkers in a highly imbalanced dataset is investigated in this paper. Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value (BVAL) Diffusion-Weighted (DW) images obtained from PROSTATEx 2016 challenge dataset publicly available in TCIA (The Cancer Imaging Archive) is used for this study. High-level features are extracted using a single layer sparse autoencoder (SAE). Synthetic Minority Oversampling Technique (SMOTE), Weka Resample algorithm and Adaptive Synthetic (ADASYN) sampling approach are explored to solve the class-imbalance problem. The performance of various classifiers are also investigated and it was found that the data augmented using ADASYN followed by classification using random forest classifier achieved the best performance. It achieved an area under ROC curve of 0.979. It also reached a Cohen's kappa score of 0.873, an accuracy of 93.65% and F-Measure of 0.94 in distinguishing clinically significant PCa from indolent PCa. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： sparse autoencoder Prostate cancer Multi-parametric MRI ADASYN Random forest

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A semi-supervised learning framework for gas chimney detection based on sparse autoencoder and TSVM

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2019年第1期16卷 52-61页

作者： Xu, Pengcheng Lu, Wenkai Wang, Benfeng Tsinghua Univ Easysignal Grp Inst Artificial IntelligenceTHUAIDept Automat State Key Lab Intelligent Technol & SystBeijing Beijing 100084 Peoples R China Tongji Univ Sch Ocean & Earth Sci Inst Adv Study State Key Lab Marine Geol Shanghai 200092 Peoples R China

Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seismic interpretation results, we take advantage of the information of unlabeled data by unsupervised feature extraction and semi-supervised classification methods. The sparse autoencoder (SAE) is an unsupervised learning method that can extract the features of data without labels, and the transductive support vector machine (TSVM) is a semi-supervised method that trains a classifier according to both labeled and unlabeled data. In this paper, we propose a semi-supervised learning framework that combines SAE and TSVM to detect gas chimneys. In this framework, SAE is used to extract features from data and TSVM is used to classify the labeled and unlabeled features. Therefore, the unlabeled data is taken advantage of in both unsupervised feature extraction and semi-supervised classification to improve accuracy. In order to improve the precision of detection, the attributes of neighborhood regions are also utilized. Due to the information learned from plenty of unlabeled data, the proposed framework performs well. Numerical experiments are carried out on sample sets and field data. The proposed framework has higher testing accuracy than the traditional MLP method, especially when the labeled training set is small. In field data experiments, the proposed framework also gets good prediction results for gas chimney locations.

关键词： semi-supervised learning sparse autoencoder transductive support vector machine gas chimney detection multi-attribute classification

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P300 based character recognition using sparse autoencoder with ensemble of SVMs

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2019年第4期39卷 956-966页

作者： Kundu, Sourav Ari, Samit Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 Odisha India

In this study, a brain-computer interface (BCI) system known as P300 speller is used to spell the word or character without any muscle activity. For P300 signal classification, feature extraction is an important step. In this work, deep feature learning techniques based on sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) are proposed for feature extraction. Deep feature provides the abstract information about the signal. This work proposes fusion of deep features with the temporal features, which provides abstract and temporal information about the EEG signal. These deep feature and temporal feature are partially complement of each other to represent the EEG signal. For classification of the EEG signal, an ensemble of support vector machines (ESVM) is adopted as it helps to reduce the classifiers variability. In classifier ensemble system, the score of individual classifier is not at the same level. To transform these scores into a common level, min-max normalization is proposed prior to combining them. Min-max normalization scales the classifiers' score between 0 and 1. The experiments are conducted on three standard public datasets, dataset IIb of BCI Competition II, dataset II of the BCI Competition III and BNCI Horizon dataset. The experimental results show that the proposed method yields better or comparable performance compared to earlier reported techniques. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Brain-computer interface Deep learning Ensemble of support vector machines Normalization P300 sparse autoencoder

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A Real-Time Visual Tracking Approach Using sparse autoencoder and Extreme Learning Machine

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UNMANNED SYSTEMS 2015年第4期3卷 267-275页

作者： Dai, Liang Zhu, Yuesheng Luo, Guibo He, Chao Lin, Hanchi Peking Univ Shenzhen Grad Sch Inst Big Data Technol Lab Commun & Informat Secur Shenzhen Peoples R China

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

关键词： Visual tracking real-time sparse autoencoder feature selection extreme learning machine

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Surface microseismic data denoising based on sparse autoencoder and Kalman filter

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2022年第1期10卷 616-628页

作者： Li, Xuegui Feng, Shuo Hou, Nan Wang, Ruyi Li, Hanyang Gao, Ming Li, Siyuan Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing Peoples R China Northeast Petr Univ Sch Comp & Informat Technol Daqing Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing Peoples R China Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya Peoples R China CNPC Engn Technol R&D Co Ltd Beijing Peoples R China

Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.

关键词： sparse autoencoder surface micro-seismic deep learning Kalman filter

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Fuzzy sparse autoencoder Framework for Single Image Per Person Face Recognition

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第8期48卷 2402-2415页

作者： Guo, Yuwei Jiao, Licheng Wang, Shuang Wang, Shuo Liu, Fang Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & ComputatJoint Xian 710071 Shaanxi Peoples R China Univ Birmingham Ctr Excellence Res Computat Intelligence & Applic Sch Comp Sci Birmingham B15 2TT W Midlands England

The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition.

关键词： Fuzzy rough set one sample per person face recognition sparse autoencoder two-layer feature learning

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A sparse autoencoder BASED HYPERSPECTRAL ANOMALY DETECTION ALGORIHTM USING RESIDUAL OF RECONSTRUCTION ERROR 39

A SPARSE AUTOENCODER BASED HYPERSPECTRAL ANOMALY DETECTION A...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chang, Shizhen Du, Bo Zhang, Liangpei Wuhan Univ Remote Sensing Grp State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

ISBN: (纸本)9781538691540

Considering the reconstruction error of autoencoder can reflect the characteristic of anomalies, this paper presents a novel hyperpsectral anomaly detection algorithm utilizes the residual of reconstruction error to estimate the anomalous information of the image. We firstly employ an adaptive dual concentric window to collect the training sets, and input pixels in the whole window and the outer window into the sparse autoencoders to learn the representations of the input, respectively. Then we use the representation parameters to reconstruct the test pixel and calculate the detection result by a deviation of reconstruction errors under two cases. The proposed sparse autoencoder-based anomaly detector experimental results have been conducted into the San Diego airport dataset and the Urban area dataset, the detection performances verified by the receiver operating characteristic curve and the area under curve show that the proposed method outperforms other representative detection methods.

关键词： Hyperspectral anomaly detection sparse autoencoder dimensional reduction

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An Automatic Feature Learning and Fault Diagnosis Method Based on Stacked sparse autoencoder 7th

An Automatic Feature Learning and Fault Diagnosis Method Bas...

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7th International Workshop of Advanced Manufacturing and Automation (IWAMA)

作者： Qi, Yumei Shen, Changqing Liu, Jie Li, Xuwei Li, Dading Zhu, Zhongkui Soochow Univ Sch Rail Transportat Suzhou 215131 Jiangsu Peoples R China

ISBN: (纸本)9789811057687;9789811057670

Fault feature extraction is the key for fault diagnosis, and automatic feature extraction has been a hot topic recently. Deep learning is a breakthrough in artificial intelligence and known as an automatic feature learning method. One of the most important aspects to measure the extracted features is sparsity. Thus, this paper presents a stacked sparse autoencoder (SAE)-based method for automatic feature extraction and fault diagnosis of rotating machinery. The penalty term of the SAE can help automatically extract sparse and representative features. Experiments and comparisons are conducted to validate the effectiveness and superiority of the proposed method. Results fully demonstrate that the stacked SAE-based diagnosis method can automatically extract more representative high-level features and perform better than the traditional intelligent fault diagnosis method like artificial neural network (ANN).

关键词： Feature extraction Fault diagnosis sparse autoencoder Deep learning

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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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