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Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 110卷 101976-101976页

作者： Macias-Garcia, Laura Martinez-Ballesteros, Maria Luna-Romera, Jose Maria Garcia-Heredia, Jose M. Garcia-Gutierrez, Jorge Riquelme-Santos, Jose C. Univ Seville Sch Comp Engn Dept Comp Languages & Syst Seville Spain Univ Seville Fac Med Dept Citol & Histol Seville Spain Univ Seville Dept Plant Biochem & Mol Biol Seville Spain

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding feature selection and generation. autoencoders (AEs) appear as a specific technique for conducting nonlinear feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annotation) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related to breast cancer recurrence.

关键词： autoencoder Breast cancer DNA methylation Feature generation Machine learning

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Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 123卷 103903-103903页

作者： Togo, Ren Watanabe, Haruna Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Educ & Res Ctr Math & Data Sci Kita Ku N-12W-7 Sapporo Hokkaido 0600812 Japan Hokkaido Univ Fac Informat Sci & Technol Kita Ku N-14W-9 Sapporo Hokkaido 0600814 Japan

Aim: The aim of this study was to determine whether our deep convolutional neural network-based anomaly detection model can distinguish differences in esophagus images and stomach images obtained from gastric X-ray examinations. Methods: A total of 6012 subjects were analyzed as our study subjects. Since the number of esophagus X-ray images is much smaller than the number of gastric X-ray images taken in X-ray examinations, we took an anomaly detection approach to realize the task of organ classification. We constructed a deep autoencoding gaussian mixture model (DAGMM) with a convolutional autoencoder architecture. The trained model can produce an anomaly score for a given test X-ray image. For comparison, the original DAGMM, AnoGAN, and a One-Class Support Vector Machine (OCSVM) that were trained with features obtained by a pre-trained Inception-v3 network were used. Results: Sensitivity, specificity, and the calculated harmonic mean of the proposed method were 0.956, 0.980, and 0.968, respectively. Those of the original DAGMM were 0.932, 0.883, and 0.907, respectively. Those of AnoGAN were 0.835, 0.833, and 0.834, respectively, and those of OCSVM were 0.932, 0.935, and 0.934, respectively. Experimental results showed the effectiveness of the proposed method for an organ classification task. Conclusion: Our deep convolutional neural network-based anomaly detection model has shown the potential for clinical use in organ classification.

关键词： Deep learning Medical image analysis Gastric X-ray examination Esophagus Stomach Anomaly detection autoencoder

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Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2019年 99卷 101701-000页

作者： Asiri, Norah Hussain, Muhammad Al Adel, Fadwa Alzaidi, Nazih King Saud Univ Comp & Informat Sci Coll Riyadh Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Med Dept Ophthalmol Riyadh Saudi Arabia Prince Mansour Mil Hosp Ophthalmol Dept At Taif Saudi Arabia

Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.

关键词： Diabetic Retinopathy Lesion Exudate Macula Diabetic macular edema Optic disc Microaneurysms Hemorrhages CNN autoencoder RNN DBN

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Deep neural network coupled with distance-based model selection for efficient history matching

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年第0期185卷 106658-000页

作者： Kim, Jaejun Park, Changhyup Lee, Kyungbook Ahn, Seongin Jang, Ilsik Seoul Natl Univ Dept Energy Syst Engn Seoul 08826 South Korea Kangwon Natl Univ Dept Energy & Resources Engn Kangwon 24341 South Korea Korea Inst Geosci & Mineral Resources Petr & Marine Res Div Daejeon 34132 South Korea Korea Inst Geosci & Mineral Resources Geo ICT Convergence Res Team Daejeon 34132 South Korea Chosun Univ Dept Energy & Resources Engn Gwangju 61452 South Korea

This paper develops a novel approach of deep neural network (DNN)-based inverse modeling with selecting more reliable supervised-learning datasets with distance-based maps allocated at individual well levels. To mitigate divergence and overshooting in multi-scaled data assimilation, the DNN-based inverse model introduces a stacked autoencoder (SAE) that reduces the dimension of the training data. The proposed workflow also implements k-medoids clustering by selecting geo-models that have dynamic performances close to the true responses of producers to obtain plausible supervised-learning datasets. History-matching accuracy and forecasting performance are investigated in comparison of typical ensemble Kalman filter (EnKF)-based data assimilation for a waterflooding problem of heterogeneous fluvial channel reservoirs. The proposed approach is capable of matching the oil production rates of all producers in the range of 1.1-11.3% mean absolute percentage error (MAPE) and can forecast the future performances within 15.5% errors, while the errors of the EnKF method are up to six times higher. The proposed workflow can estimate the water-breakthrough time and the water productions accurately by generating more reliable geo-models with geological realism.

关键词： Deep neural network autoencoder History matching Distance-based method Data assimilation

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Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems

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SENSORS 2022年第1期22卷 32-32页

作者： Liu, Tong Sabrina, Fariza Jang-Jaccard, Julian Xu, Wen Wei, Yuanyuan Massey Univ Coll Sci Auckland 0632 New Zealand Cent Queensland Univ Sch Engn & Technol Sydney NSW 2000 Australia Massey Univ Coll Sci Cybersecur Lab Auckland 0632 New Zealand

A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.

关键词： smart transport system blockchain smart contract artificial intelligence deep learning autoencoder multi-layer perceptron DDoS

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DMVSVDD: Multi-View Data Novelty Detection with Deep Autoencoding Support Vector Data Description

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Chen, Zeqiu Zhao, Kaiyi Sun, Shulin Li, Jiayao Wang, Shufan Sun, Ruizhi China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China North Univ China Sch Data Sci & Technol Taiyuan 030051 Peoples R China Minist Agr Sci Res Base Integrated Technol Precis Agr Anim Hu Beijing 100083 Peoples R China

Novelty detection is usually defined as the identification of new or abnormal objects (outliers) from the normal ones (inliers), which has wide potential applications in the real world. Recently, an effective algorithm called Deep Support Vector Data Description (Deep SVDD) has been proposed for novelty detection, which jointly trains a deep neural network while optimizing a data-enclosing hypersphere in output space. However, some constraints such as hypersphere collapse, limit the adaptability of the model and may affect the model performance. Moreover, most of the existing studies concerning Deep SVDD focus on the novelty detection for single-view data, which may fail to provide an accurate and reliable decision because the single-view data sometimes cannot fully reflect the actual condition of the problem. In this study, we developed an end-to-end deep learning method of novelty detection for multi-view data, i.e., the Deep Multi-View SVDD (DMVSVDD). To fully preserve the correlative and complementary information of multi-view data, we jointly trained multiple deep autoencoding neural networks for multiple views while adaptively optimizing the data-enclosing hypersphere of each view in latent space. A global objective function was proposed, which takes both of the sample reconstruction error minimization and the hypersphere volume minimization into consideration simultaneously to prevent hyper sphere collapse in the model. In the global objective function, the hypersphere centers and view weights of different views were designed to adaptively select the better representative features after each epoch during the training process by embedding multi-view target samples into multiple data-enclosing hyperspheres with minimum volumes. The experimental results on the MNIST-USPS and NUS-WIDE-OBJECT datasets reveal that our proposed method learns the target class effectively and is superior to some state-of-the-art methods.

关键词： Novelty detection Multi-view data Support vector data description autoencoder One-class classification

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Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第1期5卷 015033页

作者： Wang, Yanyang Li, Wei Liu, Feiyi Shen, Jianmin Cent China Normal Univ Key Lab Quark & Lepton Phys MOE Wuhan 430079 Peoples R China Cent China Normal Univ Inst Particle Phys Wuhan 430079 Peoples R China Eotvos Lorand Univ Inst Phys 1-A Pazmany P Setany H-1117 Budapest Hungary Baoshan Univ Coll Engn & Technol Baoshan Peoples R China

Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent z and spatial correlation length correlation exponent nu(perpendicular to) were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.

关键词： machine learning non-equilibrium phase transitions branching annihilating random walks convolutional neural networks autoencoder

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A Semi-Supervised Domain Alignment Transformer for Hyperspectral Images Change Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Wang, Yanheng Sha, Jianjun Gao, Lianru Zhang, Yonggang Rong, Xianhui Zhang, Ce Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Base Qingdao 266000 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China Univ Bristol Sch Geog Sci Bristol BS8 1SS England UK Ctr Ecol & Hydrol Lancaster LA1 4AP England

Supervised deep learning (DL)-based hyperspectral images change detection (HSIs-CD) has demonstrated excellent performance;however, current methods require many labeled training samples, and labeling the dataset is labor-intensive, limiting the application of high-precision supervised learning. Besides, there has been a lack of breakthroughs in unsupervised HSIs change detection (CD) methods due to the different feature distributions of bitemporal HSIs. Here, we propose a semi-supervised domain alignment transformer (DA-Former) for HSIs-CD to address the issues with limited samples. Specifically, a dual-branch transformer autoencoder (TAE) is designed, where the middle layer weights of the dual-branch transformer are shared, pulling features from different data into the same space. Moreover, the TAE is also trained cyclically to align the domains. Although the bitemporal HSIs features are cross domain, there is still confusion between the features of different objects. Thus, two fully connected (FC) layers are employed to classify the HSIs middle features extracted by the TAE into changed class or unchanged class with limited labeled data. Three HSIs-CD datasets are used to test this method, showing that TAE can align bitemporal HSIs domains and achieve the highest accuracy compared with benchmark approaches.

关键词： autoencoder change detection (CD) domain alignment hyperspectral images (HSIs) transformer

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Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 185卷

作者： Zheng, Xiaochen Arrazola, Pedro Perez, Roberto Echebarria, Daniel Kiritsis, Dimitris Aristimuno, Patxi Saez-de-Buruaga, Mikel Swiss Fed Inst Technol Lausanne EPFL CH-1015 Lausanne Switzerland Fac Engn Mondragon Unibertsitatea Arrasate Mondragon 20500 Spain GF Machining Solut CH-2504 Biel Switzerland Unimetrik SA Legutiano 01170 Spain

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data -driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are based on external sensors, such as ac-celerometers and microphones, which require installation of extra devices. Some recent studies have proved the feasibility of online chatter detection using internal signals such as drive motor current. This study aims to investigate the effectiveness of different internal signals extracted from CNC system for chatter detection and compare them with external acceleration signals. The external and internal signals are first compared with time-frequency analysis using Discrete Fourier Transform and Ensemble Empirical Mode Decomposition approaches. Two chatter detection methods are then presented based on manually and automatically extracted features respectively. The first method uses two nonlinear dimensionless indicators, C0 complexity and Power Spectral Entropy, of filtered signals. The second approach uses autoencoder for automatic feature extraction and Support Vector Machine as classifier for chatter identification. A series of milling experiments are conducted and chatters are intentionally created by changing the milling process parameters. Multiple internal signals are collected using software provided by the machine manufacturer. Results show that several internal CNC signals, such as the nominal current signal and the actual torque signal, can achieve comparable performance to external signals for chatter detection.

关键词： Chatter detection Milling process Internal signal Empirical mode decomposition autoencoder

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Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期206卷

作者： Derraz, Radhwane Muharam, Farrah Melissa Jaafar, Noraini Ahmad Yap, Ng Keng Putra Malaysia Univ Dept Agr Technol Bangi 43400 Selangor Malaysia Putra Malaysia Univ Dept Land Management Bangi 43400 Selangor Malaysia Putra Malaysia Univ Inst Math Res Bangi 43400 Selangor Malaysia

Biomass estimation, fertilisation, and crop production reflect crop yield potential. The prediction of these var-iables allows the selection of crop cultivars with high yield potential. Deep neural networks (DNNs) can predict such crop variables. However, DNNs are data greedy algorithms that overfit/underfit on small-size datasets. Additionally, the collection of big data is expensive and laborious. Therefore, providing synthetic big data is preferable. This study aims to: (i) develop a trigonometric-Euclidean-smoother interpolation (TESI) for contin-uous time-series and non-time-series data augmentation to prevent DNNs from under/overfitting;(ii) compare the TESI performance to the tabular variational autoencoder (TVAE) and the conditional tabular generative adversarial network (CTGAN);and (iii) compare the DNN performance before and after data augmentation. Two time-series datasets, oil palm production and rice production, and two non-time-series datasets, fertiliser and rice total aboveground biomass (TAGB), were augmented using the TESI, TVAE, and CTGAN algorithms. The TESI retained the features' original probability distribution in the four datasets. The C-TESI achieved the lowest mean squared error mean percentage (MAEP) on the oil palm (0.60-2.85%), rice (0.77-1.72%), and fertiliser datasets (2.04-2.21%). The TESI retained the variance inflation factor (VIF) ranges less than 10 on the four datasets;the TESI retained a VIF range of 1.99-10.06 or reduced the VIF range to 1.55-6.66. Furthermore, the TESI retained the Spearman's r (rs) range of 0.79-0.97 or increased it to 0.81-0.99 on the four datasets. The DNN achieved the highest coefficient of determination (R2) (0.77-0.99) and lowest root mean squared error (RMSE) ranges (2.8E+01-8.1E+05) on the four datasets augmented with the TESI. The Q-TESI, C-TESI, and L-TESI overcame the LN-TESI in retaining the features' original probability distribution, minimising the augmentation loss, reducing the VIF, increasing

关键词： Continuous data augmentation Deep neural network autoencoder Smart farming Machine learning algorithm Generative adversarial network

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