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Spectral-Spatial Feature Extraction With Dual Graph autoencoder for Hyperspectral Image Clustering

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8500-8511页

作者： Zhang, Yongshan Wang, Yang Chen, Xiaohong Jiang, Xinwei Zhou, Yicong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China

autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative representations with the assumption that connected samples share the same label and they cannot directly embed the geometric structure into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE) to learn discriminative representations for HSIs. Utilizing the relationships of pair-wise pixels within homogenous regions and pair-wise spectral bands, DGAE first constructs the superpixel-based similarity graph with spatial information and band-based similarity graph to characterize the geometric structures of HSIs. With the developed dual graph convolution, more discriminative feature representations are learnt from the hidden layer via the encoder-decoder structure of DGAE. The main advantage of DGAE is that it fully exploits both the geometric structures of pixels with spatial information and spectral bands to promote nonlinear feature extraction of HSIs. Experiments on HSI datasets show the superiority of the proposed DGAE over the state-of-the-art methods.

关键词： Feature extraction Data mining Principal component analysis Hyperspectral imaging Convolution Task analysis Decoding Hyperspectral imagery dimensionality reduction feature extraction autoencoder graph convolution

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Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第6期20卷 1827-1840页

作者： Xu, Xinyao Qin, Fangbo Zhao, Wenjun Xu, De Wang, Xingang Yang, Xihao Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China State Key Lab Smart Mfg Special Vehicles & Transm Baotou City 014000 Inner Mongolia Peoples R China

The anomaly detection for multimode industrial process is a challenging problem, because the multiple operation modes present various main distributions of monitored variables, and the dynamic sequential characteristics exist within each operation mode. This paper proposes an anomaly detection method based on sequence-to-sequence gated recurrent units (SGRU). First, to better model both the cross-mode trends and mode-specific sequential characteristics, a main reconstruction module and residual reconstruction module are integrated to improve the ability to represent complex process. Both modules are implemented by SGRUs. Second, a reconstruction error prediction module is designed to estimate the mean values of mode-specific reconstruction errors, which helps to determine the more reliable alarm thresholds. Third, the two anomaly indicators are utilized to represent the deviation degree of monitored variables against the normal conditions, according to the statistical errors and biases of reconstructions, respectively. The effectiveness of the proposed method is validated on simulations with multimode process, and on the practical data set collected from the Cleaning-in-Place multimode process of an aseptic beverage filling line in a real factory.

关键词： Anomaly detection autoencoder gated recurrent unit multimode process

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GenCoder: A Novel Convolutional Neural Network Based autoencoder for Genomic Sequence Data Compression

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第3期21卷 405-415页

作者： Sheena, K. S. Nair, Madhu S. Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 India

Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term storage and analysis of genomic sequence data are required. Data-specific compression algorithms can effectively manage a large volume of data. In recent times, deep learning has achieved great success in many compression tools and is gradually being used in genomic sequence compression. Significantly, autoencoder has been applied in dimensionality reduction, compact representations of data, and generative model learning. It can use convolutional layers to learn essential features from input data, which is better for image and series data. autoencoder reconstructs the input data with some loss of information. Since accuracy is critical in genomic data, compressed genomic data must be decompressed without any information loss. We introduce a new scheme to address the loss incurred in the decompressed data of the autoencoder. This paper proposes a novel algorithm called GenCoder for reference-free compression of genomic sequences using a convolutional autoencoder and regenerating the genomic sequences from a latent code produced by the autoencoder, and retrieving original data losslessly. Performance evaluation is conducted on various genomes and benchmarked datasets. The experimental results on the tested data demonstrate that the deep learning model used in the proposed compression algorithm generalizes well for genomic sequence data and achieves a compression gain of 27% over the best state-of-the-art method.

关键词： Genomics Bioinformatics Sequential analysis DNA Costs Compressors Encoding Genomic sequence deep learning autoencoder FASTA lossless compression

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Enhancing image classification using adaptive convolutional autoencoder-based snow avalanches algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第10期18卷 6867-6879页

作者： Dhiravidachelvi, E. Devadas, T. Joshva Kumar, P. J. Sathish Pandi, S. Senthil Mohamed Sathak Engn Coll Dept Elect & Commun Engn Kilakarai Tamil Nadu India VIT Univ Sch Comp Sci & Engn Vellore India Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

The disease that causes a large number of deaths annually across the world is brain cancer and it has become an important research topic in the field of medical image processing in recent times. There are various techniques for the detection of brain tumors (BT) but magnetic resonance imaging (MRI) diagnosing techniques show superior performance in the prognosis and examination of brain tumors in the early stages. The manual detection of brain tumors by radiologists leads to many limitations like errors and lack of detection accuracy. Hence, there is a need for computer-aided diagnostic techniques to help radiologists in detecting brain tumors accurately from the MRI images. To make this process more effective, the implementation of an automated technique is a preferred choice. In this paper, an effective detection and classification technique Adaptive convolutional autoencoder-based Snow Avalanches (ACAE-SA) Algorithm is proposed. This algorithm comprises an Adaptive CNN component and an autoencoder to detect and categorize BT from the MRI images. To mitigate the computational complexities in these components a Snow Avalanches algorithm is integrated into this work as an optimization technique. For the validation of the proposed architecture two MRI image datasets namely figshare and BraTS 2018 are used. The proposed technique proved its effectiveness in the detection and classification of brain tumors from the MRI images and outperformed the state-of-the-art techniques.

关键词： Adaptive CNN autoencoder Brain tumor Medical image processing MRI images Snow avalanches algorithm

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Evolving Domain Generalization via Latent Structure-Aware Sequential autoencoder

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 14514-14527页

作者： Qin, Tiexin Wang, Shiqi Li, Haoliang City Univ Hong Kong Dept Elect & Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Comp Sci Kowloon Hong Kong Peoples R China

Domain generalization (DG) refers to the problem of generalizing machine learning systems to out-of-distribution (OOD) data with knowledge learned from several provided source domains. Most prior works confine themselves to stationary and discrete environments to tackle such generalization issue arising fromOODdata. However, in practice, manytasks in non-stationary environments (e.g., autonomous-driving car system, sensor measurement) involve more complex and continuously evolving domain drift, emerging new challenges for model deployment. In this paper, we first formulate this setting as the problem of evolving domain generalization. To deal with the continuously changing domains, we propose MMD-LSAE, a novel framework that learns to capture the evolving patterns among domains for better generalization. Specifically, MMD-LSAE characterizes OOD data in non-stationary environments with two types of distribution shifts: covariate shift and concept shift, and employs deep autoencoder modules to infer their dynamics in latent space separately. In these modules, the inferred posterior distributions of latent codes are optimized to align with their corresponding prior distributions via minimizing maximum mean discrepancy (MMD). We theoretically verify that MMD-LSAE has the inherent capability to implicitly facilitate mutual information maximization, which can promote superior representation learning and improved generalization of themodel. Furthermore, the experimental results on both synthetic and real-world datasets show that our proposed approach can consistently achieve favorable performance based on the evolving domain generalization setting.

关键词： Domain generalization non-stationary environments autoencoder kernel methods

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Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

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INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023年第3期58卷 1284-1298页

作者： Munyendo, Leah Njoroge, Daniel Zhang, Yanyan Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci Garbenstr 23 D-70599 Stuttgart Germany Dedan Kimathi Univ Technol Inst Food Bioresources Technol Dedan Kimathi 10143 Nyeri Kenya Univ Hohenheim Dept Flavor Chem Fruwirthstr 12 D-70599 Stuttgart Germany

Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta-adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.

关键词： adulteration autoencoder chicory coffee geographical origin NIR spectroscopy

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High Accuracy Individual Identification Model of Crested Ibis <italic>(Nipponia Nippon)</italic>Based on autoencoder With Self-Attention

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IEEE ACCESS 2020年 8卷 41062-41070页

作者： Xie, Jiangjian Yang, Jun Ding, Changqing Li, Wenbin Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab Natl Forestry & Grass Land Adm Forestry E Beijing 100083 Peoples R China Beijing Forestry Univ Sch Ecol & Nat Conservat Beijing 100083 Peoples R China

As the population and the distribution of Crested Ibis (Nipponia nippon) become larger, it is necessary to propose a highly efficient census method to estimate the population size of the Crested Ibis. Passive acoustic monitoring (PAM) has a very good prospect for the Crested Ibis monitoring. To realize the automatic census of the Crested Ibis with PAM, the automatic individual identification method based on the vocalization is the key technology. A novel individual identification model was proposed in this paper, which built the autoencoder based on LSTM to obtain the meaningful latent representation from the raw recording directly, further, embedded self-attention and putted forward a combined training mode to achieve distinctive latent representation. With this model, nine Crested Ibis individuals were identified accurately, the highest accuracy is 0.971, and the average accuracy reaches 0.958. As for other three species, Little owl (Athene noctua), Chiffchaff (Phylloscopus collybita) and Tree pipit (Anthus trivialis), the better performances were achieved than the existing method, which means the proposed model can provide an alternative method for the individual identification of other bird species.

关键词： Nipponia nippon individual identification autoencoder self-attention LSTM

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Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 247卷 108114-108114页

作者： Dai, Peishan Shi, Yun Lu, Da Zhou, Ying Luo, Jialin He, Zhuang Chen, Zailiang Zou, Beiji Tang, Hui Huang, Zhongchao Liao, Shenghui Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Dept Psychiat Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Natl Clin Res Ctr Mental Disorders Changsha 410083 Hunan Peoples R China Cent South Univ Sch Basic Med Sci Dept Biomed Engn Changsha 410083 Hunan Peoples R China

Background and objective: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. Methods: We developed a Residual Denoising autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over -sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. Results: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. Conclusions: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

关键词： Recurrent major depressive disorder Functional magnetic resonance imaging Functional connectivity autoencoder Residual network Deep learning

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Signal-to-noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder

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JOURNAL OF RAMAN SPECTROSCOPY 2021年第4期52卷 890-900页

作者： Fan, Xian-guang Zeng, Yingjie Zhi, Yu-Liang Nie, Ting Xu, Ying-jie Wang, Xin Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361102 Fujian Peoples R China Xiamen Key Lab Optoelect Transducer Technol Fujian Key Lab Univ & Coll Transducer Technol Xiamen Peoples R China

The signal-noise ratio plays a key role in acquiring plentiful chemical structural information in the Raman spectrometer. The miniature spectrometer is generally compact at the expense of performance. In this work, we proposed a compact, signal-to-noise ratio (SNR) enhancement of the Raman spectrometer by the optimization of optical structure and a noise reduction method. Concerning its optical structure, the Raman spectrometer is increasing the intensity by adding an off-the-shelf cylindrical lens. On the other side of the algorithm, a relevant automatic denoising method of convolutional denoising autoencoder (CDAE) is proposed to further advance the SNR in Raman spectra without manual intervention. The results indicate the performance of the compact Raman spectrometer could increase to a certain extent by testing with 785 nm laser and Ne/Ar source. Besides, by using CDAE to deal with contaminated Raman spectra, a higher SNR is obtained. The results demonstrate that the improvement of the hardware and algorithm is effective for removing the noisy Raman signal and achieving higher SNR. This result may be helpful in further improving the performance of integrated Raman spectrometers and research on miniaturized instruments.

关键词： autoencoder convolutional neural network Czerny-Turner denoising Raman spectrometer

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Disentangled-Multimodal Adversarial autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第12期39卷 4137-4149页

作者： Hu, Dan Zhang, Han Wu, Zhengwang Wang, Fan Wang, Li Smith, J. Keith Lin, Weili Li, Gang Shen, Dinggang Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated o

关键词： Functional magnetic resonance imaging Brain modeling Predictive models Data models Biological system modeling Biomarkers Infant age prediction autoencoder multimodal machine learning magnetic resonance imaging

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