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Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2017年第8期12卷 1816-1829页

作者： Qin, Huafeng El-Yacoubi, Mounim A. Univ Paris Saclay CNRS Telecom SudParis SAMOVAR F-91011 Paris France Chongqing Technol & Business Univ Chongqing 400067 Peoples R China

Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.

关键词： Hand biometrics finger-vein verification deep learning convolutional neural network convolutional autoencoder representation learning

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MS²A-Net: Multiscale Spectral-Spatial Association Network for Hyperspectral Image Clustering

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 6518-6530页

作者： Shahi, Kasra Rafiezadeh Ghamisi, Pedram Rasti, Behnood Gloaguen, Richard Scheunders, Paul Univ Antwerp Dept Phys Imec Visionlab B-2000 Antwerp Belgium Helmholtz Inst Freiberg Resource Technol Helmholtz Zentrum Dresden Rossendorf D-09599 Freiberg Germany Inst Adv Res Artificial Intelligence A-1030 Vienna Austria

Remote sensing hyperspectral cameras acquire high spectral-resolution data that reveal valuable composition information on the targets (e.g., for Earth observation and environmental applications). The intrinsic high dimensionality and the lack of sufficient numbers of labeled/training samples prevent efficient processing of hyperspectral images (HSIs). HSI clustering can alleviate these limitations. In this study, we propose a multiscale spectral-spatial association network (MS(2)A-Net) to cluster HSIs. The backbone of MS(2)A-Net is an autoencoder architecture that allows the network to capture the nonlinear relation between data points in an unsupervised manner. The network applies a multistream approach. One stream extracts spectral information by deploying a spectral association unit. The other stream derives multiscale contextual and spatial information by employing dilated (atrous) convolutional kernels. The obtained feature representation generated by MS(2)A-Net is fed into a standard k-means clustering algorithm to produce the final clustering result. Extensive experiments on four HSIs for different types of applications (i.e., geological-, rural-, and urban-mapping) demonstrate the superior performance of MS(2)A-Net over the state-of-the-art shallow/deep learning-based clustering approaches in terms of clustering accuracy.

关键词： convolutional autoencoder deep learning dilated (atrous) convolutions hyperspectral imaging multiscale information remote sensing spectral association

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Quantifying Asymmetry Between Medial and Lateral Compartment Knee Loading Forces Using Acoustic Emissions

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第4期69卷 1541-1551页

作者： Jeong, Hyeon Ki An, Sungtae Herrin, Kinsey Scherpereel, Keaton Young, Aaron Inan, Omer T. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA

Objective: Osteoarthritis is the most common type of knee arthritis that can be affected by excessive and compressive loads and can affect one or more compartments of the knee: medial, lateral, and patellofemoral. The medial compartment tends to be the most vulnerable to injuries and research suggests that a better understanding of the medial to lateral load distribution conditions could provide insights to the quantitative usage of knee compartments in activities of daily life. Methods: Prior to study in an osteoarthritic clinical population which may present with various complicating anatomical and physiological changes, we investigate knee acoustical emissions of able-bodied individuals during a varying width squat exercise which simulates loading asymmetries that would typically be seen in this clinical population. To that end, we present a novel method to quantify the directional bias of asymmetry between the medial and lateral compartment knee joint load in healthy individuals by recording knee acoustical emissions and analyzing them using a deep neural network in a subject independent model. We placed four miniature contact microphones on the medial and lateral sides of the patella on both the left and right leg. We compared the handcrafted audio features with the automated features extracted from the convolutional autoencoder which is an unsupervised model that learns the comprehensive representation of the input to determine whether these automated features can better represent the signal's characteristic in regard to the structural asymmetry of the knee joint. The input to the convolutional auto encoder (CAE) is a time-frequency representation and different types of these images such as spectrogram and scalogram are compared. We alsocompared the multi-sensor fusion approach with the performance of a single sensor to determine the robustness of using multiple sensors. Results: Using a representation learning based approach, we developed a subject independen

关键词： Knee Feature extraction Force Joints Electromyography Loading Legged locomotion convolutional autoencoder joint acoustic emissions joint contact force neural network scalogram sensor fusion

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Multi-stream pyramid collaborative network for spectral unmixing

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第8期45卷 2674-2701页

作者： Wang, Jie Ni, Mengying Wang, Zhixiang Yan, Yu Cheng, Xiang Xu, Jindong Yantai Univ Sch Comp & Control Engn Yantai Peoples R China Peking Univ Sch Informat Sci & Technol Beijing Peoples R China

convolutional autoencoder, which can well model the spatial correlation of the data, have been widely applied to spectral unmixing task and achieved desirable performance. However, the fixed geometric structure of convolution kernels makes it difficult to capture global context. To address this issue, strategies such as dilated convolution or transformer are often employed, but this may result in minor loss of local details. Therefore, we propose a collaborative unmixing network with a multi-scale pyramid structure to capture both global and local features simultaneously. To integrate features from different scales in the unmixing process, we employ a cross-stream fusion feature strategy, which not only promote collaborative representations but also capture long-range dependencies while preserving local details. Meanwhile, we also design the residual spectral attention mechanism to refine the features from different scales and facilitate their fine-grained fusion. In the proposed network, each convolutional stream undergoes effective collaborative training using a convolutional autoencoder structure. The collaborative strategies include cross-stream feature fusion mechanism and alternating training strategy with weight sharing for endmember information. Experiments over three real hyperspectral datasets indicate the effectiveness of our method compared to other unmixing techniques.

关键词： Spectral unmixing deep learning convolutional autoencoder multi-scale feature fusion collaborative strategies

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Unsupervised annual change detection from optical-SAR fused satellite image time-series using 3D-CAE

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第5期44卷 1628-1642页

作者： Paul, Subir la Rosa, Laura Elena Cue Ghamisi, Pedram Gloaguen, Richard Helmholtz Inst Freiberg Resource Technol H Helmholtz Zent Dresden Rossendorf HZDR Freiberg Germany Inst Adv Res Artificial Intelligence IARAI Vienna Austria Helmholtz Inst Freiberg Resource Technol H Helmholtz Zent Dresden Rossendorf HZDR D-09599 Freiberg Germany

Satellite images are widely used for change detection in various applications, like, agricultural and forest cover area monitoring and management, urban planning, disaster management etc. which require prior information about the timeline of changes. However, it is very challenging to identify precise timeline of changes and to acquire corresponding labelled reference of changes. Considering this, convolutional Auto-Encoder (CAE)-based approaches has been proposed for unsupervised change detection from bi-temporal remote sensing images. However, these type of approaches require prior knowledge about the exact moment the change occurs in order to employ the bi-temporal images, one pre-change and one post-change. This work introduces the utilization of satellite image time-series (SITS) data in a Joint 3D CAE-based unsupervised approach to detect annual changes. Our study focuses on a mining area where the changes mostly occurs due to tailings, waste and ore deposit, mining pit, mining residue, and so on. Here, we have explored Synthetic Aperture Radar (SAR) (i.e. Sentinel-1) data along with optical (i.e. Sentinel-2) data, since optical data alone was not able to detect all the changes. The recall of detected change areas during 2016-2018 has improved by about 16% with the use of combination of Sentinel-1&2 data as compared to utilization of only Sentinel-2 images.

关键词： convolutional autoencoder optical-SAR fusion satellite image time-series unsupervised change detection

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Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

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IEEE ACCESS 2021年 9卷 39995-40007页

作者： Kwak, Mingu Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul 02841 South Korea

Detecting an anomaly in multichannel signal data is a challenging task in various domains. It should take into account the cross-channel relationship and temporal relationship within each channel. Moreover, the signal data is high-dimensional and making it difficult to gather sufficient abnormal labels. Consequently, unsupervised reconstruction-based anomaly detection methods have been applied successfully in many studies. However, they lose valuable channel information inherent in the reconstruction errors by merely averaging the errors for both the channel and time, then consider the average value as an anomaly score. In this study, we propose a method to explicitly employ channelwise reconstruction errors as a feature to detect abnormal signals. After a convolutional autoencoder produces the channelwise reconstruction errors, a machine learning anomaly detection model aggregates the errors as an anomaly score. To demonstrate the effectiveness and applicability of the proposed model, we conduct experiments using simulated data and real-world automobile data. The results show that the proposed method remarkably enhances the detectability compared to the simple average of the reconstruction errors. The reconstruction errors of abnormal and normal channels are shown to be different;therefore, it can be considered as an appropriate feature for anomaly detection. The best performance is obtained by using local outlier factors in the following anomaly detection model.

关键词： Anomaly detection Image reconstruction Feature extraction Data models Time series analysis Principal component analysis Convolution Anomaly detection convolutional autoencoder deep learning multichannel sensor signal data unsupervised learning

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EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

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IEEE ACCESS 2021年 9卷 58667-58676页

作者： Silva, Francisco Pereira, Tania Morgado, Joana Frade, Julieta Mendes, Jose Freitas, Claudia Negrao, Eduardo De Lima, Beatriz Flor Silva, Miguel Correia Da Madureira, Antonio J. Ramos, Isabel Hespanhol, Venceslau Costa, Jose Luis Cunha, Antonio Oliveira, Helder P. Inst Syst & Comp Engn Technol & Sci INESC TEC P-4200465 Porto Portugal Univ Porto FEUP Fac Engn P-4200465 Porto Portugal Univ Porto FCUP Fac Sci P-4169007 Porto Portugal Ctr Hosp & Univ Sao Joao CHUSJ P-4200319 Porto Portugal Univ Porto FMUP Fac Med P-4200319 Porto Portugal Univ Porto Inst Invest & Inovacao Saude i3S P-4200135 Porto Portugal Univ Porto IPATIMUP Inst Mol Pathol & Immunol P-4200135 Porto Portugal Univ Tras Os Montes & Alto Douro UTAD P-5001801 Vila Real Portugal

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A convolutional autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.

关键词： Computed tomography Lung Feature extraction Lung cancer Transfer learning Databases Tumors convolutional autoencoder EGFR prediction lung cancer transfer learning unsupervised feature learning

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Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第5期39卷 1545-1557页

作者： Zreik, Majd van Hamersvelt, Robbert W. Khalili, Nadieh Wolterink, Jelmer M. Voskuil, Michiel Viergever, Max A. Leiner, Tim Isgum, Ivana Univ Med Ctr Utrecht Image Sci Inst NL-3584 CS Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiol NL-3584 CS Utrecht Netherlands Univ Amsterdam Dept Biomed Engn & Phys Med Ctr NL-1105 AZ Amsterdam Netherlands Univ Med Ctr Utrecht Dept Cardiol NL-3584 CS Utrecht Netherlands Univ Utrecht NL-3512 JE Utrecht Netherlands Univ Amsterdam Dept Radiol & Nucl Med Med Ctr NL-1105 AZ Amsterdam Netherlands

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81 +/- 0.02 on the artery-level, and 0.87 +/- 0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.

关键词： Functionally significant coronary artery stenosis convolutional autoencoder convolutional neural network fractional flow reserve coronary CT angiography deep learning

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Quality grading method for Pleurotus eryngii during postharvest storage based on hyperspectral imaging and multiple quality indicators

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FOOD CONTROL 2024年 166卷

作者： Wei, Ziyuan Liu, Haoling Xu, Jinghua Li, Yihang Hu, Jin Tian, Shijie Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China

During storage, Pleurotus eryngii is prone to spoilage, significantly affecting their commercial value. This study addresses the issue of inconsistent quality grading standards and low detection accuracy for Pleurotus eryngii. . It proposes a method for grading the quality of Pleurotus eryngii during post-harvest low-temperature (4 degrees C) storage, achieving rapid quality grading based on hyperspectral imaging technology combined with clustering algorithms and comprehensive evaluation methods. According to 11 physical and chemical indexes analyzed by K-means algorithm and principal component analysis, the samples were divided into three categories: high, medium and inferior. These clustering results were then used as labels to build classification models. We compared the feature extraction capabilities of convolutional autoencoder and competitive adaptive reweighted sampling, as well as the modeling effectiveness of support vector machine, linear discriminant analysis, and partial least squares discriminant analysis. The models' practicality was validated using external data. The results showed that the convolutional autoencoder-support vector machine model performed the best in quality classification, with an accuracy of 91.58%, an F1 score of 91.36%, a precision of 89.65%, and a recall of 90.60%. In summary, this study demonstrates the rationality of using clustering algorithms for quality grading of Pleurotus eryngii and highlights the significant improvement in prediction accuracy and generalization ability achieved by convolutional autoencoder.

关键词： Pleurotus eryngii K -means Principal component analysis Hyperspectral imaging convolutional autoencoder

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An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第2期11卷 446-452页

作者： Dhikhi, T. Saravanan, M. S. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Chennai Tamil Nadu India

The necessity for safety of information in a network has inflated due to the impressive growth of web applications. Several methods of intrusion detection are used to detect irregularities which depend on precision, detection frequency, other parameters and are anticipated to familiarize to vigorously varying risk scenes. To accomplish consistent abnormalities detection in a network many machine learning algorithms have been formulated by researchers. A technique based on unsupervised machine learning that use two separate machine learning algorithms to identify anomalies in a network viz convolutional autoencoder and softmax classifier is proposed. These profound models were skilled as well as evaluated on NSLKDD test data sets on the NSLKDD training dataset. Using well-known classification metrics such as accuracy, precision and recall, these machine learning models were assessed. The developed intrusion detection system model experimental findings showed promising outcomes in anomaly detection systems for real-world implementation and is compared with the prevailing definitive machine learning techniques. This strategy increases the detection of network intrusion and offers a renewed intrusion detection study method.

关键词： Intrusion detection machine learning deep learning convolutional autoencoder softmax classifier NSL-KDD dataset

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