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Mitotic Index Determination on Live Cells From Label-Free Acquired Quantitative Phase Images Using a supervised autoencoder

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第6期18卷 2828-2834页

作者： Pognonec, Philippe Gustovic, Axel Djabari, Zied Pourcher, Thierry Barlaud, Michel Univ Cote dAzur Biol F-06103 Nice France Univ Cote dAzur Sci Fac Sci F-06103 Nice France Univ Cote dAzur Med Fac Med F-06103 Nice France Univ Cote dAzur Fac Sci I3S Lab F-06103 Nice France

This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population.

关键词： Cells (biology) Noninvasive treatment Statistics Indexes Fluorescence Biology DNA Mitotic index non-invasive cell monitoring quantitative phase imaging (QPI) supervised autoencoder

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Fishing Gear Pattern Recognition by Including supervised autoencoder Dimensional Reduction

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Carlos, Hugo Aranda, Ramon Rivera-De Velasco, Mariana Rodriguez-Gonzalez, Ansel Y. Elena Mendez-Lopez, Maria Consejo Nacl Ciencia & Tecnol CONACYT Ciudad De Mexico 03940 Mexico Ctr Invest Ciencias Informac Geoespacial Unidad Yucatan Merida 97000 Yucatan Mexico Ctr Invest Cient & Educ Super Ensenada CICESE Unidad Transferencia Tecnol Tepic UT3 Tepic 63173 Mexico

Fishing is a crucial worldwide activity as it provides a source of food and economic income. A challenge in ecology and conservation is decreasing overfishing and illegal, unreported, and unregulated fishing (IUUF). One strategy to decrease those issues is to track vessels for detecting fishing behaviors through monitory systems. In this letter, we present an approach to classify fishing behaviors, specifically, for four fishing gear types (trawl, purse seine, fixed gear, and longline) using automatic identification systems (AISs) data from the Global Fishing Watch platform. Thus, our main contribution is how we propose data processing by including a supervised autoencoder dimensional reduction (SA-DR) processing data step. This step allows removing redundant features and noise, avoiding overfitting, decreasing data complexity, and preserving the differences between classes. Specifically, we propose to use IVIS and centroid encoder (CE) methods. The experimental results show how our approach applying SA-DR over the vessel trajectory feature representation reduces the variation results among different classifiers and achieves a high classification accuracy of up to 95%. This result could help prevent IUUF, overfishing, and improve fishery management strategies.

关键词： Gears Trajectory Artificial intelligence Marine vehicles Monitoring Support vector machines Proposals Dimension reduction fishing gear supervised autoencoder vessel behavior

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A personalized classification model using similarity learning via supervised autoencoder

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Jo, Hyunjae Jun, Chi-Hyuck POSTECH Dept Ind & Management Engn Pohang 37673 South Korea

Personalized modeling usually trains a predictive model for a new point using only observations similar to the new point. However, existing methodologies have limitations that do not reflect the target variable in the similarity calculation nor the density of neighbors. Thus, this paper proposes a new personalized modeling method. The proposed methodology transforms the input variables into the latent variables through a supervised autoencoder and calculates the similarity measure between observations in the transformed latent space. The proposed method also considers the neighborhood density around the test point. As a result of the experiments with real datasets, it was found that the proposed method outperformed other benchmark methods and showed the interpretability of the predictive model.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Classification Latent variables Personalized model Similarity learning supervised autoencoder

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Multi-label Learning via supervised autoencoder 37

Multi-label Learning via Supervised Autoencoder

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37th Chinese Control Conference (CCC)

作者： Lian, Siming Liu, Jianwei Lu, Runkun Luo, Xionglin China Univ Petr Dept Automat Beijing Campus CUP Beijing 102249 Peoples R China

ISBN: (纸本)9789881563958

Multi-label learning has been applied in various areas. One crucial research issue in multi-label learning is how to find a complex nonlinear mapping between instances and multi-label such that the instances with unknown labels can be predicted with the mapping. In this paper, we propose a multi-label learning method based on supervised deep autoencoder, and study the effects of the various combinations of the constraints of hidden layers and output layers. In the output layer, the sum-square error of the true labels and estimating labels is minimized through backpropagation-through-time learning algorithm. Extensive experiments conducted on eight real-world datasets demonstrate the effectiveness of our proposed method compared with several slate-of-art baseline methods.

关键词： Multi-Label Learning Nonlinear Mapping supervised autoencoder

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Multi-label Learning via supervised autoencoder

Multi-label Learning via Supervised Autoencoder

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第37届中国控制会议

作者： Siming Lian Jianwei Liu Runkun Lu Xionglin Luo Department of Automation China University of Petroleum Beijing Campus （CUP）

关键词： Multi-Label Learning Nonlinear Mapping supervised autoencoder

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Pulmonary nodule diagnosis using dual-modal supervised autoencoder based on extreme learning machine

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EXPERT SYSTEMS 2017年第6期34卷 n/a-N.PAG页

作者： Qiang, Yan Ge, Lei Zhao, Xin Zhang, Xiaolong Tang, Xiaoxian Taiyuan Univ Technol TYUT Coll Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Penn State Univ Coll Informat Sci & Technol 307D IST Bldg University Pk PA 16802 USA Shanxi Prov Peoples Hosp Taiyuan 030012 Shanxi Peoples R China

In recent years, deep learning techniques have been applied to the diagnosis of pulmonary nodules. In order to improve the pulmonary nodule diagnostic performance effectively, we propose a novel pulmonary nodule diagnosis method using dual-modal deep supervised autoencoder based on extreme learning machine for which discriminative features are automatically learnt from the input data. The network is fed with nodule images in pairs obtained from computed tomography and positron emission tomography respectively. For each pair image, the high-level discriminative features of nodules in computed tomography and positron emission tomography are extracted from stacked supervised autoencoder layers. The outputs of the proposed architecture are combined using an ideal fusion method to get the final classification. In the experiments, 5-fold cross-validation method is used to validate the proposed method on 1,600 pulmonary nodule images and our method reaches high-classification sensitivities of 91.75% at 1.58 false positives per scan. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.

关键词： computed tomography dual-modal deep learning extreme learning machine positron emission tomography pulmonary nodule supervised autoencoder

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Deep autoencoder With Orthogonal Features for Process Monitoring 21

Deep Autoencoder With Orthogonal Features for Process Monito...

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IEEE 21st International Conference on Industrial Informatics (INDIN)

作者： Yang, Chao Zhang, Congcong Wang, Junxiang Liu, Qiang Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9781665493130

With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults are quality-relevant. To address this issue, an orthogonal feature separation autoencoder (OFSAE) method is developed for quality-relevant fault monitoring. The proposed OFSAE mainly consists of the quality-relevant encoder network, quality-irrelevant encoder network, decoder network, and regression network. Through parallel learning and orthogonal projection for process variables, quality-relevant and quality-irrelevant variations can be isolated while maintaining good prediction performance. Finally, in comparison with conventional monitoring methods, the superiority of OFSAE is validated by the Tennessee Eastman process.

关键词： process monitoring autoencoder supervised autoencoder orthogonal projection information separation

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Brain tumor detection using deep ensemble model with wavelet features

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HEALTH AND TECHNOLOGY 2022年第6期12卷 1157-1167页

作者： Sahoo, Debendra Kumar Das, Abhishek Mishra, Satyasis Mohanty, Mihir Narayan Centurion Univ Technol & Management Bhubaneswar Odisha India Siksha O Anusandhan Deemed Univ ITER Bhubaneswar Odisha India

Purpose A person's healthy activities are determined by the state of his or her brain. The brain is in charge of all of a person's activities. If a small abnormality develops in the brain, it will have a negative impact on the person regardless of whether the other organs are in good condition. As a result, early detection of any abnormal growth in the brain is essential. Methods In this work, the authors have utilized data pre-processing using discrete wavelet transform (DWT) and segmentation, whereas, for detection, an ensemble learning technique is proposed. DWT and segmentation help in increasing the dataset size that is used to train the deep learning model. Segmentation using supervised Auto-encoder (AE) is used for data enhancement to strengthen the training process. The original data, outputs of DWT, and segmented images are utilized for the training of the ensemble model designed with three parallel-connected convolutional neural networks (CNNs). Results The detection results obtained from the ensemble of these recurrent models are then passed through the Multilayer Perceptron (MLP) for final detection. Kaggle brain MRI image dataset is used to complete the proposed method. Test accuracy, F1-score, precision, sensitivity, and specificity provided by this method are 98.08%, 0.9836, 1.0000, 0.9677, and 1.0000 respectively. In comparison to state-of-the-art models, the proposed model produces competitive outcomes. Conclusion In time detection of the tumor may lead to the survival of the patient. Automatic and accurate detection is another perspective of this field. For this purpose, we have proposed a deep ensemble model with wavelet features. The ensemble model provides increased performance in comparison to single models due to the parallel training.

关键词： Brain tumor detection Discrete wavelet transform supervised autoencoder Convolutional neural network Ensemble learning

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Adversarial-Free Speaker Identity-Invariant Representation Learning for Automatic Dysarthric Speech Classification 23

Adversarial-Free Speaker Identity-Invariant Representation L...

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Interspeech Conference

作者： Janbakhshi, Parvaneh Kodrasi, Ina Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique to learn speaker identity-invariant representations for dysarthric speech classification is based on adversarial training. However, adversarial training can be challenging, unstable, and sensitive to training parameters. To avoid adversarial training, in this paper we propose to learn speaker-identity invariant representations exploiting a feature separation framework relying on mutual information minimization. Experimental results on a database of neurotypical and dysarthric speech show that the proposed adversarial-free framework successfully learns speaker identity-invariant representations. Further, it is shown that such representations result in a similar dysarthric speech classification performance as the representations obtained using adversarial training, while the training procedure is more stable and less sensitive to training parameters.

关键词： Parkinson's disease speaker identity feature separation supervised autoencoder mutual information

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Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions

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COGNITIVE COMPUTATION 2021年第5期13卷 1154-1171页

作者： Villatoro-Tello, Esau Parida, Shantipriya Kumar, Sajit Motlicek, Petr Univ Autonoma Metropolitana Unidad Cuajimalpa Mexico City DF Mexico Idiap Res Inst Rue Marconi 19 CH-1920 Martigny Switzerland GreatLearning Bangalore Karnataka India

According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed to be rather stable personality characteristics. Normally, implicit motives are obtained by Operant Motives, unconscious intrinsic desires measured by the Operant Motive Test (OMT). The OMT test requires participants to write freely descriptions associated with a set of provided images and questions. In this work, we explore different recent machine learning techniques and various text representation techniques for facing the problem of the OMT classification task. We focused on advanced language representations (e.g, BERT, XLM, and DistilBERT) and deep supervised autoencoders for solving the OMT task. We performed an exhaustive analysis and compared their performance against fully connected neural networks and traditional support vector classifiers. Our comparative study highlights the importance of BERT which outperforms the traditional machine learning techniques by a relative improvement of 7.9%. In addition, we performed an analysis of how the BERT attention mechanism is being modified. Our findings indicate that the writing style features acquire higher importance at the moment of accurately identifying the different OMT categories. This is the first time that a study to determine the performance of different transformer-based architectures in the OMT task is performed. Similarly, our work propose, for the first time, using deep supervised autoencoders in the OMT classification task. Our experiments demonstrate that transformer-based methods exhibit the best empirical results, obtaining a relative improvement of 7.9% over the competitive baseline suggested as part of the GermEval 2020 challenge. Additionally, we show that features associated with the writing style are more important than content-based words. Some of these findings show strong conn

关键词： Operant motive test Psycholinguistics BERT supervised autoencoder Deep learning Natural language processing

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