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Joint Triplet autoencoder for histopathological colon cancer nuclei retrieval

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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MMD-encouraging convolutional autoencoder: a novel classification algorithm for imbalanced data

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APPLIED INTELLIGENCE 2021年第10期51卷 7384-7401页

作者： Li, Bin Gong, Xiaofeng Wang, Chen Wu, Ruijuan Bian, Tong Li, Yanming Wang, Zhiyuan Luo, Ruisen Sichuan Univ Coll Elect Engn 24 South Sect 1One Ring Rd Chengdu Peoples R China Rutgers Univ New Brunswick Dept Comp Sci Piscataway NJ 08854 USA Chengdu Dagongbochuang Informat Technol Co Ltd Chengdu 610059 Peoples R China

Imbalanced data classification problem is widely existed in commercial activities and social production. It refers to the scenarios with considerable gap of sample amount among classes, thus significantly deteriorating the performance of the traditional classification algorithms. The previous dealing methods often focus on resampling and algorithm adjustment, but ignore enhancing the ability of feature learning. In this study, we have proposed a novel algorithm for imbalanced data classification: Maximum Mean Discrepancy-Encouraging Convolutional autoencoder (MMD-CAE), from the perspective of feature learning. The algorithm adopts a two-phase target training process. The cross entropy loss is employed to calculate reconstruction loss of data, and the Maximum Mean Discrepancy (MMD) with intra-variance constraint is used to stimulate the feature discrepancy in bottleneck layer. By encouraging maximization of MMD between two-class samples, and mapping the original space to a higher dimension space via kernel skills, the features can be learned to form a more effective feature space. The proposed algorithm is tested on ten groups of samples with different imbalance ratios. The performance metrics of recall rate, F1 score, G-means and AUC verify that the proposed algorithm surpasses the existing state-of-the-art methods in this field, also with stronger generalization ability. This study could shed new lights on the related studies in terms of constituting more effective feature space via the proposed MMD with intra-variance constraint method, and the holistic MMD-CAE algorithm for imbalanced data classification.

关键词： Imbalanced data autoencoder Two-phase target training Maximum mean discrepancy Inter-class distance

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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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Residual autoencoder Deep Neural Network for Electrical Capacitance Tomography

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Computers, Materials & Continua 2022年第12期73卷 6307-6326页

作者： Wael Deabes Kheir Eddine Bouazza Department of Computer Science in Jamoum Umm Al-Qura UniversityMakkah25371Saudi Arabia Computers and Systems Engineering Department Mansoura UniversityMansoura35516Egypt Laboratoire D’Informatique et des Technologies de L’Information D’Oran(LITIO) University of Oran31000OranAlgeria

Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography(ECT)image ***,there is still a need to make these image reconstruction results faster and of better ***,Deep Learning(DL)is flourishing and is adopted in many *** DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks(ANNs).An ECT image reconstruction model using DNN is proposed in this *** proposed model mainly uses Residual autoencoder called(ECT_ResAE).Alarge-scale dataset of 320 k instances have been generated to train and test the proposed ECT_ResAE *** instance contains two vectors;a distinct permittivity distribution and its corresponding capacitance *** capacitance vector has been modulated to generate a 66×66 image,and represented to the ECT_ResAE as an *** scalability and practicability of the ECT_ResAE network are tested using noisy data,new samples,and experimental *** experimental results show that the proposed ECT_ResAE image reconstruction model provides accurate reconstructed *** achieved an average image Correlation Coefficient(CC)of more than 99%and an averageRelative ImageError(IE)around 8.5%.

关键词： ECT image reconstruction deep learning residual autoencoder IE CC

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An autoencoder-like deep NMF representation learning algorithm for clustering

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Wang, Dexian Zhang, Pengfei Deng, Ping Wu, Qiaofeng Chen, Wei Jiang, Tao Huang, Wei Li, Tianrui Chengdu Univ Tradit Chinese Med Chengdu 611137 Peoples R China Xihua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Peoples R China Mfg Ind Chains Collaborat & Informat Support Techn Chengdu 611756 Peoples R China

Clustering plays a crucial role in the field of data mining, where deep non-negative matrix factorization (NMF) has attracted significant attention due to its effective data representation. However, deep matrix factorization based on autoencoder is typically constructed using multi-layer matrix factorization, which ignores nonlinear mapping and lacks learning rate to guide the update. To address these issues, this paper proposes an autoencoder-like deep NMF representation learning (ADNRL) algorithm for clustering. First, according to the principle of autoencoder, construct the objective function based on NMF. Then, decouple the elements in the matrix and apply the nonlinear activation function to enforce non-negative constraints on the elements. Subsequently, the gradient values corresponding to the elements update guided by the learning rate are transformed into the weight values. This weight values are combined with the activation function to construct the ADNRL deep network, and the objective function is minimized through the learning of the network. Finally, extensive experiments are conducted on eight datasets, and the results demonstrate the superior performance of ADNRL.

关键词： Non-negative matrix factorization Clustering autoencoder Deep learning

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Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse autoencoder

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IEEE ACCESS 2019年 7卷 63346-63360页

作者： Ayinde, Babajide O. Barnawi, Abdulaziz Y. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran 31261 Saudi Arabia Univ Louisville Dept Elect & Comp Engn Louisville KY 40292 USA

The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of a few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. The experimental results indicate the superiority of our approach compared with existing approaches in terms of accuracy and lifetime extension factor.

关键词： autoencoder energy conservation energy management unsupervised feature extraction feature importance feature ranking lifetime extension wireless sensor network deep learning

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Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 5761-5774页

作者： Chen, Li Saykin, Andrew J. Yao, Bing Zhao, Fengdi Univ Florida Dept Biostat Gainesville FL 32603 USA Indiana Univ Sch Med Dept Radiol & Imaging Sci Indianapolis IN 46202 USA Emory Univ Dept Human Genet Atlanta GA 30322 USA

Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomark-ers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. Here, we develop two multi-task deep autoencoders, which are based on the convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representation by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal DNA methylation data collected from the peripheral blood in Alzheimer's Disease Neuroimaging Initiative, we demonstrate that the proposed multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal DNA methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only the histor-ical DNA methylation data and the performance is further improved by including all temporal DNA methylation data. Availability:: https://***/lichen-lab/MTAE. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning Alzheimer?s disease Longitudinal data autoencoder DNA methylation

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LTScoder: Long-Term Time Series Forecasting Based on a Linear autoencoder Architecture

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IEEE ACCESS 2024年 12卷 98623-98633页

作者： Kim, Geunyong Yoo, Hark Kim, Chorwon Kim, Ryangsoo Kim, Sungchang Elect & Telecommun Res Inst Honam Res Div Gwangju 34129 South Korea

A long-term time series forecasting (LTSF) model named LTScoder based on a linear autoencoder architecture is presented in this paper. LTScoder performs feature extraction through an encoder to generate a latent vector and conducts time series prediction based on this latent vector through a decoder. The latent vector, which is mapped to a lower dimensionality by extracting features from the input time series, not only reduces the complexity of the linear regression operations performed in the decoder but also captures essential temporal dependencies for forecasting. Experimental results conducted on popular univariate and multivariate datasets with diverse patterns demonstrate that LTScoder achieves up to 16.95 times faster inference time than PatchTST, the state-of-the-art transformer-based model. LTScoder exhibits minimal degradation in prediction accuracy while using only 2.11% of PatchTST's parameters. Furthermore, we show that the LTScoder model is well-suited for edge computing in time series data prediction tasks. The architectural flexibility of LTScoder allows the encoder and decoder to be deployed in separate systems, enhancing scalability and efficiency. We measure the performance of LTScoder in terms of its prediction accuracy and computational complexity using the ETRI edge gateway system.

关键词： Time series analysis Predictive models Transformers Data models Computational modeling Forecasting Feature extraction autoencoders autoencoder edge computing long-term time series forecasting LTScoder

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Spatial-Aware Hyperspectral Nonlinear Unmixing autoencoder With Endmember Number Estimation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 20-41页

作者： Shahid, Kazi Tanzeem Schizas, Ioannis D. Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

In this article, we develop a novel fully unsupervised autoencoder-based scheme for nonlinear hyperspectral pixel unmixing. A unique approach is derived where high noise and unresponsive pixels are accounted for, by a unique averaging approach based on spatially aware filters built using radial basis function (RBF) kernels. A novel technique is implemented via calculating rank-equivalent kernel covariance matrices in order to estimate the unknown number of endmembers contributing to the data. Utilization of spatial information is done via RBF-based weighted averaging, which is then followed by endmember estimation via K-means clustering. The RBF distances from the cluster centers are determined to measure the position of the mixed pixels in relation to the centers, which is utilized as a preliminary estimation of the abundances. The proposed framework is robust in the presence of unresponsive pixels, while highly versatile working with different nonlinear unmixing models. Extensive numerical tests establish the superiority of the novel approach with respect to state-of-the-art methods.

关键词： Hyperspectral imaging Estimation Kernel Spatial resolution Earth Recurrent neural networks Meteorology autoencoder hyperspectral imaging neural networks nonlinear unmixing radial basis function (RBF) kernel unsupervised

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Analysis of different RNN autoencoder variants for time series classification and machine prognostics

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 149卷 107322-107322页

作者： Yu, Wennian Kim, Il Yong Mechefske, Chris Chongqing Univ Coll Mech Engn Chongqing 400044 Peoples R China Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada

Recurrent neural network (RNN) based autoencoders, trained in an unsupervised manner, have been widely used to generate fixed-dimensional vector representations or embeddings for varying length multivariate time series. These embeddings have been demonstrated to be useful for time series reconstruction, classification, and creation of health index (HI) curves of machines being used in industrial applications, based on which the remaining useful life (RUL) of machines can be estimated. In this study, we extend the traditional form of RNN autoencoders as a feature extractor for multivariate time series to a more general form in terms of arranging the order of input or output sequences and the hidden unit architecture. We apply the embeddings obtained by different variants of RNN autoencoders for a time series classification task and a machine RUL estimation problem using two publicly available datasets. A random research strategy is used to find the optimal hyperparameters of all variants for each task in order to give a fair comparison of the general performance among different variants over a large hyperparameter space, as well as the best performance that each variant can achieve compared with the best reported values in the literature. Our results show that traditional reversing the order of output time series while maintaining the order of input time series when training an RNN autoencoder does not show improved performance for the two studied cases. Thus, intentionally arranging the input or output order seems unnecessary for training the RNN autoencoder as a feature extractor of time series. We further observe that only the RNN architectures with gating mechanism can achieve the functionality of encoding for the time series, and none of the three common gated architectures we studied shows significantly and consistently improved performance compared to the others on the two studied cases. However, the bidirectional RNN autoencoders yield slightly better perform

关键词： Recurrent neural network autoencoder Time series Remaining useful life Random search

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