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Attention-Guided autoencoder for Automated Progression Prediction of Subjective Cognitive Decline With Structural MRI

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第6期27卷 2980-2989页

作者： Guan, Hao Yue, Ling Yap, Pew-Thian Xiao, Shifu Bozoki, Andrea Liu, Mingxia Univ North Carolina Chapel Hill Dept Radiol Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill Biomed Res Imaging Ctr Chapel Hill NC 27599 USA Shanghai Jiao Tong Univ Shanghai Mental Hlth Ctr Dept Geriatr Psychiat Sch Med Shanghai 200030 Peoples R China Univ North Carolina Chapel Hill Dept Neurol Chapel Hill NC 27599 USA

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD) which happens even earlier than mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques (e.g., structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem and lack interpretability. To this end, we propose an interpretable autoencoder model with domain transfer learning (IADT) for progression prediction of SCD. Firstly, the proposed model can leverage MRIs from both the target domain (i.e., SCD) and auxiliary domains (e.g., AD and NC) for progressive SCD identification. Besides, it can automatically locate the disease-related brain regions of interest (defined in brain atlases) through an attention mechanism, which shows good interpretability. In addition, the IADT model is straightforward to train and test with only 5 similar to 10 seconds on CPUs and is suitable for medical tasks with small datasets. Extensive experiments on the publicly available ADNI dataset and a private CLAS dataset have demonstrated the effectiveness of the proposed method.

关键词： Subjective cognitive decline Alzheimer's disease domain adaptation MRI autoencoder interpretability

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Classification of data on stacked autoencoder using modified sigmoid activation function

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1-18页

作者： Kumar, Arvind Sodhi, Sartaj Singh Comp Sci & Engn USICT GGSIPU Delhi India

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.

关键词： autoencoder sigmoid activation function logsigmoid neural network classification stacked autoencoder

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Advanced autoencoder Transfer Function Parameter Extraction Technique for Neuro-TF Parametric Modeling of Microwave Components

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IEEE MICROWAVE AND WIRELESS TECHNOLOGY LETTERS 2024年第5期34卷 459-462页

作者： Liu, Jinyi Feng, Feng Liu, Wei Xue, Jianguo Liu, Shaochang Gao, Fang Li, Xiaolong Zhang, Qi-Jun Tianjin Univ Sch Microelect Tianjin Key Lab Imaging & Sensing Microelect Techn Tianjin 300072 Peoples R China Carleton Univ Dept Elect Ottawa ON K1S 5B6 Canada

Recently, neuro-transfer function (neuro-TF) has become a recognized method for electromagnetic (EM) parametric modeling. The existing neuro-TF methods use the vector fitting technique to perform transfer function (TF) parameter extraction, commonly encountering nonsmoothness and discontinuity issues for the extracted TF parameters with respect to geometrical parameters. This letter proposes an advanced autoencoder TF parameter extraction technique for neuro-TF parametric modeling of microwave components. In the proposed technique, the autoencoder is introduced to extract a set of TF parameters as TF parameters from the $S$ -parameters with the encoder part and generate a decoder function as the TF in the original neuro-TF model. The TF parameters extracted using the proposed technique behave much smoother than the TF parameters extracted using traditional vector fitting. Meanwhile, the proposed technique avoids the discontinuity problem caused by vector fitting in the standard neuro-TF method. Parametric modeling using the smooth TF parameters can thus have higher accuracy than modeling with nonsmooth TF parameters. The proposed technique is demonstrated by two examples of EM parametric modeling of microwave components.

关键词： Microwave filters Parameter extraction Vectors Fitting Neural networks Parametric statistics Decoding autoencoder microwave components neural networks parameter extraction parametric modeling

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Robust deep image clustering using convolutional autoencoder with separable discrete Krawtchouk and Hahn orthogonal moments

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2024年 22卷

作者： Bouali, Aymane El Ouariachi, Ilham Zahi, Azeddine Zenkouar, Khalid Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Lab Intelligent Syst & Applicat LSIA Fes Morocco Univ Moulay Ismail Fac Sci Dept Comp Sci Meknes Morocco

By cooperatively learning features and assigning clusters, deep clustering is superior to conventional clustering algorithms. Numerous deep clustering algorithms have been developed for a variety of application levels;however, the majority are still incapable of learning robust noise-resistant latent features, which limits the clustering performance. To address this open research challenge, we introduce, for the first time, a new approach called: Robust Deep Embedded Image Clustering algorithm with Separable Krawtchouk and Hahn Moments (RDEICSKHM). Our approach leverages the advantages of Krawtchouk and Hahn moments, such as local feature extraction, discrete orthogonality, and noise tolerance, to obtain a meaningful and robust image representation. Moreover, we employ LayerNormalization to further improve the latent space quality and facilitate the clustering process. We evaluate our approach on four image datasets: MNIST, MNIST-test, USPS, and Fashion-MNIST. We compare our method with several deep clustering methods based on two metrics: clustering accuracy (ACC) and normalized mutual information (NMI). The experimental results show that our method achieves superior or competitive performance on all datasets, demonstrating its effectiveness and robustness for deep image clustering.

关键词： Deep clustering autoencoder Discrete separable orthogonal moments Hahn moments Krawtchouk moments Image clustering

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Joint Triplet autoencoder for histopathological colon cancer nuclei retrieval

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 1063-1082页

作者： Singh, Satya Rajendra Dubey, Shiv Ram Shruthi, M. S. Ventrapragada, Sairathan Dasharatha, Saivamshi Salla Indian Inst Informat Technol Comp Vis Grp Sri City Andhra Prades India Indian Inst Informat Technol Comp Vis & Biometr Lab Allahabad Uttar Pradesh India

Deep learning has shown a great improvement in the performance of visual tasks. Image retrieval is the task of extracting the visually similar images from a database for a query image. The feature matching is performed to rank the images. Various hand-designed features have been derived in past to represent the images. Nowadays, the power of deep learning is being utilized for automatic feature learning from data in the field of biomedical image analysis. autoencoder and Siamese networks are two deep learning models to learn the latent space (i.e., features or embedding). autoencoder works based on the reconstruction of the image from latent space. Siamese network utilizes the triplets to learn the intra-class similarity and inter-class dissimilarity. Moreover, autoencoder is unsupervised, whereas Siamese network is supervised. We propose a Joint Triplet autoencoder Network (JTANet) by facilitating the triplet learning in autoencoder framework. A joint supervised learning for Siamese network and unsupervised learning for autoencoder is performed. Moreover, the Encoder network of autoencoder is shared with Siamese network and referred as the Siamcoder network. The features are extracted by using the trained Siamcoder network for retrieval purpose. The experiments are performed over Histopathological Routine Colon Cancer dataset. We have observed the promising performance using the proposed JTANet model against the autoencoder and Siamese models for colon cancer nuclei retrieval in histopathological images.

关键词： Retrieval Siamese autoencoder Deep learning Colon cancer

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Prototype-Guided autoencoder for OCT-Based Fingerprint Presentation Attack Detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023年 18卷 3461-3475页

作者： Liu, Yi-Peng Zuo, Wangyang Liang, Ronghua Sun, Haohao Li, Zhanqing Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Anti-spoofing ability is vital for fingerprint identification systems. Conventional fingerprint scanning devices can only obtain information from the fingertip surfaces, and their performance is susceptible to skin conditions and presentation attacks (PAs). However, optical coherence tomography (OCT) can scan subcutaneous tissue and obtain 3D fingerprint structures, naturally enhancing its PA detection (PAD) ability from the perspective of hardware. Existing unsupervised PAD methods are based on image reconstruction. However, the reconstruction error is easily affected by OCT noise and the rich details of OCT images. Therefore we propose feature-based reconstruction to alleviate this problem, called the prototype-guided autoencoder. The model consists of a memory module and a denoising autoencoder without the requirement of PA fingerprints. As only bona fide fingerprints are available during the training phase, the memory module contains the prototype features of the bona fide fingerprints. During the inference phase, as the prototype memory module is frozen, the reconstructed representation of the bona fide input is close to the bona fide fingerprint features. Calculating the distance between the original features and the prototype reconstructed representation of the sample can achieve PAD. To obtain a better decision making boundary, we propose a representation consistency constraint, which reduces the bona fide representation reconstruction distance closer, so that it is easier to differentiate between fingerprints and PAs.

关键词： Fingerprint recognition Feature extraction Image reconstruction Prototypes Memory modules Training Noise reduction Presentation attack detection optical coherence tomography autoencoder prototype

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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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An interpretable health indicator for bearing condition monitoring based on semi-supervised autoencoder latent space variance maximization

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125135-125135页

作者： Chen, Xieyi Wang, Yi Meng, Lihua Qin, Yi Tang, Baoping Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Effective health indicator (HI) construction can help equipment managers detect the abnormal state of rotating machinery quickly. However, although the current deep learning-based HI construction methods have good life prediction value, most of them lose the ability to detect device anomalies and little work has been done on model interpretability. Therefore, an interpretable HI construction method based on semi-supervised autoencoder (AE) latent space variance maximization (SSALSVM) was proposed to monitor the health status of bearings. In order to fully excavate degradation features inside the device and make the model focus on the encoding process, a deep convolutional neural network (DCNN) is used as the encoding layer, while only a layer of fully-connected layer is used as the decoding layer. In addition, to enable the latent space to capture the device early degradation point (EDP) successfully, an auxiliary layer is added to the output of the encoder layer. Simultaneously, for improving the sensitivity of the indicator to capture equipment abnormal state and highlight the difference between equipment health state and degradation state, the constraint of variance maximization is added into the latent space. The model optimizing process was presented by observing the projected variance of the test set in latent space of each epoch model. The validity of the proposed HI was verified by comparison experiments on two datasets.

关键词： interpretable health indicator semi-supervised autoencoder hidden space variance maximization autoencoder early degradation point

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Application of Extreme Learning Machine- autoencoder to Medium Term Electricity Price Forecasting

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第6期59卷 7214-7223页

作者： Najafi, Arsalan Homaee, Omid Golshan, Mehdi Jasinski, Michal Leonowicz, Zbigniew Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland Islamic Azad Univ Dept Comp Engn Sepidan Branch Sepidan *** Iran VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Elect Power Engn Ostrava 70800 Czech Republic

Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary which makes forecasting very complicated. Although accurate forecasting plays a crucial role in the electricity market for traders, retailers, large consumers as well as generation companies in terms of economic efficiency and power systems safety. Hence, this article proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind this is to use trained weights for hidden layers instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. Then, the obtained output weights are used again as the input weights for a new ELM network. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on the previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.

关键词： Forecasting electricity price extreme learning machine autoencoder electricity market

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Perturbation AUTOVC: Voice Conversion From Perturbation and autoencoder Loss

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IEEE ACCESS 2023年 11卷 140174-140185页

作者： Park, Hwa-Young Lee, Young Han Chun, Chanjun Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Korea Elect Technol Inst KETI Intelligent Image Proc Res Ctr Seongnam 13509 South Korea

AUTOVC is a voice-conversion method that performs self-reconstruction using an autoencoder structure for zero-shot voice conversion. AUTOVC has the advantage of being easy and simple to learn because it only uses the autoencoder loss for learning. However, it performs voice conversion by disentangling speech information from speakers and linguistic information by adjusting the bottleneck dimension;this requires highly meticulous fine tuning of the bottleneck dimension and involves a tradeoff between speech quality and speaker similarity. To address these issues, neural analysis and synthesis (NANSY)-a fully self-supervised learning system that uses perturbations to extract speech features-is proposed. NANSY solves the problem of the adjustment of the bottleneck dimension by utilizing perturbation and exhibits high-reconstruction performance. In this study, we propose perturbation AUTOVC, a voice conversion method that utilizes the structure of AUTOVC and the perturbation of NANSY. The proposed method applies perturbations to speech signals (such as NANSY signals) to solve the problem of the voice conversion method using bottleneck dimensions. Perturbation is applied to remove the speaker-dependent information present in the speech, leaving only the linguistic information, which is then passed through a content encoder and modeled as a content embedding containing only the linguistic information. To obtain speaker information, we used x-vectors, which are extensively used in pretrained speaker recognition. The concatenated linguistic and speaker information extracted from the encoder and additional energy information is used as input to the decoder to perform self-reconstruction. Similar to AUTOVC, it is easy and simple to learn using only the autoencoder loss. For the evaluation, we measured three objective evaluation metrics: character error rate (%), cosine similarity, and short-time objective intelligibility, as well as a subjective evaluation metric: mean opinio

关键词： autoencoder information perturbation speech signal processing voice conversion

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