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CT data Curation for Liver Patients: Phase recognition in Dynamic Contrast-Enhanced CT 1st

CT Data Curation for Liver Patients: Phase Recognition in Dy...

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1st MICCAI workshop on Domain Adaptation and Representation Transfer (DART) / 1st international workshop on Medical Image learning with Less Labels and Imperfect data (MIL3ID)

作者： Zhou, Bo Harrison, Adam P. Yao, Jiawen Cheng, Chi-Tung Xiao, Jing Liao, Chien-Hung Lu, Le Yale Univ Biomed Engn New Haven CT USA PAII Inc Bethesda MD 20817 USA PingAn Technol Shenzhen Peoples R China Chang Gung Mem Hosp Linkou Taiwan

ISBN: (纸本)9783030333904;9783030333911

As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital picture archiving and communication systems (PACSs). This is the focus of our work, where we present a principled data curation tool to extract multi-phase computed tomography (CT) liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset. Emulating a typical deployment scenario, we first obtain a set of noisy labels from our institutional partners that are text mined using simple rules from DICOM tags. We train a deep learning system, using a customized and streamlined 3D squeeze and excitation (SE) architecture, to identify non-contrast, arterial, venous, and delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as "contrast". Extensive experiments on a dataset of 43K scans of 7680 patient imaging studies demonstrate that our 3DSE architecture, armed with our aggregated loss, can achieve a mean F1 of 0.977 and can correctly harvest up to 92.7% of studies, which significantly outperforms the text-mined and standard-loss approach, and also outperforms other, and more complex, model architectures.

关键词： data curation PACS Dynamic CT Phase recognition

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Improving Multi-scale Face recognition Using VGGFace2 2nd

Improving Multi-scale Face Recognition Using VGGFace2

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Massoli, Fabio Valerio Amato, Giuseppe Falchi, Fabrizio Gennaro, Claudio Vairo, Claudio ISTI-CNR via G. Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9783030307530

Convolutional neural networks have reached extremely high performances on the Face recognition task. These models are commonly trained by using high-resolution images and for this reason, their discrimination ability is usually degraded when they are tested against low-resolution images. Thus, Low-Resolution Face recognition remains an open challenge for deep learning models. Such a scenario is of particular interest for surveillance systems in which it usually happens that a low-resolution probe has to be matched with higher resolution galleries. This task can be especially hard to accomplish since the probe can have resolutions as low as 8, 16 and 24 pixels per side while the typical input of state-of-the-art neural network is 224. In this paper, we described the training campaign we used to fine-tune a ResNet-50 architecture, with Squeeze-and-Excitation blocks, on the tasks of very low and mixed resolutions face recognition. For the training process we used the VGGFace2 dataset and then we tested the performance of the final model on the IJB-B dataset;in particular, we tested the neural network on the 1:1 verification task. In our experiments we considered two different scenarios: (1) probe and gallery with same resolution;(2) probe and gallery with mixed resolutions. Experimental results show that with our approach it is possible to improve upon state-of-the-art models performance on the low and mixed resolution face recognition tasks with a negligible loss at very high resolutions. © 2019, Springer Nature Switzerland AG.

关键词： Face recognition

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Multidimensional wavelet neuron for pattern recognition tasks in the internet of things applications 1st

Multidimensional wavelet neuron for pattern recognition task...

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1st international Conference on Computer Science, Engineering and Education Applications, ICCSEEA2018

作者： Vynokurova, Olena Peleshko, Dmytro Oskerko, Semen Lutsan, Vitalii Peleshko, Marta IT Step University 83a Zamarstynivs’ka str. Lviv79019 Ukraine Lviv State University of Life Safety 35 Kleparivska Lviv79000 Ukraine

ISBN: (纸本)9783319910079

data mining and processing of Big data is key problem in developing intelligent Internet of Things (IoT) applications. In this article, the multidimensional wavelet neuron for pattern recognition tasks is proposed. Also, the learning algorithm based on the quadratic error criterion is synthesized. This approach combines the benefits of the neural networks, the neuro-fuzzy system, and the wavelet functions approximation. The proposed multidimensional wavelet neuron can be used to solve a very large class of information processing problems for the Internet of Things applications when signals are fed in online mode from many sensors. The proposed approach is uncomplicated for computational realization and can be implemented in hardware for IoT systems. The proposed learning algorithm is characterized by a high rate of convergence and high approximation properties. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Internet of things

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Improving Detection Accuracy for Imbalanced Network Intrusion Classification using Cluster-based Under-sampling with Random Forests 1

Improving Detection Accuracy for Imbalanced Network Intrusio...

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1st international Conference on Advances in Science, Engineering and Robotics Technology, ICASERT 2019

作者： Miah, Md. Ochiuddin Khan, Sakib Shahriar Shatabda, Swakkhar Farid, Dewan Md. United International University Department of Computer Science Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9781728134451

Network intrusion classification i n t he imbalanced big data environment becomes a significant and important issue in information and communications technology (ICT) in this digital era. Presently, intrusion detection systems (IDSs) are commonly using tool to detect and prevent internal and external network attacks/intrusions. IDSs are majorly bifurcated into host-based and network-based systems, and use pattern-matching techniques to detect intrusions that known as misuse-based intrusion detection system. machine learning (ML) and data mining (DM) algorithms are widely using for classifying intrusions in IDS over the last few decades. One of the major challenges for building IDS employing machine learning and data mining algorithms is to improve the intrusion classification accuracy and also reducing the false-positive rate. In this paper, we have introduced a new method for improving detection rate to classify minority-class network attacks/ intrusions using cluster-based under-sampling with Random Forest classifier. The proposed method is a multi-layer classification approach, which can process the highly imbalanced big data to correctly identify the minority/ rare class-intrusions. Initially, the proposed method classify a data point/ incoming data is attack/ intrusion or not (like normal behaviour), if it's an attack then the proposed method try to classify attack type and later sub-attack type. We have used cluster-based under-sampling technique to deal with class-imbalanced problem and popular ensemble classifier Random Forest for addressing overfitting problem. We have used KDD99 intrusion detection benchmark dataset for experimental analysis and tested the performance of proposed method with existing machine learning algorithms like: Artificial N eural Network (ANN), naïve Bayes (NB) classifier, Random Forest, and Bagging techniques. © 2019 IEEE.

关键词： Classification (of information)

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An Improved Pre-processing machine learning Approach for Cross-Sectional MR Imaging of Demented Older Adults 1

An Improved Pre-processing Machine Learning Approach for Cro...

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1st international Conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Khan, Afreen Zubair, Swaleha Al Sabri, Muaadhabdo Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9781728144870

data pre-processing is the foremost step employed in building any machine learning (ML) model. It has a significant effect on the generalization performance of the model. In the present study, we have attempted to present the data pre-processing techniques for analysis of cross-sectional Magnetic Resonance Imaging (MRI) data of demented and non-demented older adults. The MRI dataset consists of 434 MR sessions of 416 subjects, aged between 18 to 96 years. Before performing classification of MRI data and its pattern analysis, characteristics of the dataset, such as sampling, imbalanced dataset, missing values, outliers, incompleteness, and the presence of irrelevant features, had been addressed. We involved ten major steps to process dataset for the ML model building. Experimental results on the cross-sectional data indicated a significant relative improvement in the pattern analysis achieved by doing PCA (Principal Component Analysis). pattern analysis employing PCA resulted in noteworthy advancement in pattern recognition with an explained variance of 0.97377. © 2019 IEEE.

关键词： Magnetic resonance imaging

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A learning Approach for Informative-Frame Selection in US Rheumatology Images 2nd

A Learning Approach for Informative-Frame Selection in US Rh...

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作者： Fiorentino, Maria Chiara Moccia, Sara Cipolletta, Edoardo Filippucci, Emilio Frontoni, Emanuele Department of Information Engineering Università Politecnica delle Marche Ancona Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Rheumatology Università Politecnica delle Marche Ancona Italy

ISBN: (纸本)9783030307530

Rheumatoid arthritis (RA) is an autoimmune disorder that causes pain, swelling and stiffness in joints. Nowadays, ultrasound (US) has undergone an increasing role in RA screening since it is a powerful tool to assess disease activity. However, obtaining a good quality US frame is a tricky operator dependent procedure. For this reason, the purpose of this paper is to present a strategy to the automatic selection of informative US rheumatology images by means of Convolutional Neural Networks (CNNs). The proposed method is based on VGG16 and Inception V3 CNNs, which are fine tuned to classify 214 balanced metacarpal head US images (75% used for training and 25% used for testing). A repeated 3 fold cross validation for each CNN was performed. The best results were achieved with VGG16 (area under the curve = 90%). These results support the possibility of applying this method in the actual clinical practice for supporting the diagnostic process and helping young residents’ training. © 2019, Springer Nature Switzerland AG.

关键词： Ultrasonics

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1st international Conference on Big data Analysis and Deep learning, ICBDL 2018

1st International Conference on Big Data Analysis and Deep L...

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1st international Conference on Big data Analysis and Deep learning, ICBDL 2018

ISBN: (纸本)9789811308680

The proceedings contain 42 papers. The special focus in this conference is on Big data Analysis and Deep learning. The topics include: Multi-label land cover indices classification of satellite images using deep learning;real-time hand pose recognition using faster region-based convolutional neural network;school mapping for schools of basic education in Myanmar;GBSO-RSS: GPU-based BSO for rules space summarization;machine learning based live VM migration for efficient cloud data center;dynamic replication management scheme for distributed file system;Frequent pattern mining for dynamic database by using hadoop GM-Tree and GTree;investigation of the use of learning management system (moodle) in University of Computer studies, Mandalay;user preference information retrieval by using multiplicative adaptive refinement search algorithm;predictive big data analytics using multiple linear regression model;proposed framework for stochastic parsing of Myanmar language;FDTD based numerical calculation of electromagnetic wave radiation in multi-layer circular cylindrical human head;improved convergence in eddy-current analysis by singular value decomposition of subdomain problem;development and validation of parallel acoustic analysis method for the sound field design of a large space;secret audio messages hiding in images;Location based personal task reminder system using GPS technology;front caster capable of reducing horizontal forces on step climbing;mobile location based indexing for range searching;building travel speed estimation model for Yangon City from public transport trajectory data;comparison between block-encoding and quadtree compression methods for raster maps;evaluation for teacher’s ability and forecasting student’s career based on big data;behavior analysis for nursing home monitoring system.

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Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and machine learning 15

Detecting Factors Responsible for Diabetes Prevalence in Nig...

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15th Int Conf on Network and Serv Management (CNSM) / 1st Int workshop on Analyt for Serv and Application Management (AnServApp) / Int workshop on High-Precision Networks Operat and Control, Segment Routing and Serv Function Chaining (HiPNet+SR/SFC)

作者： Oyebode, Oladapo Orji, Rita Dalhousie Univ Fac Comp Sci Halifax NS Canada

ISBN: (纸本)9783903176249

Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country's diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms;hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.

关键词： Diabetes Prevalence factors Social media Text mining machine learning Intervention design

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An Efficient Predictive Analysis Model of Customer Purchase Behavior using Random Forest and XGBoost Algorithm

An Efficient Predictive Analysis Model of Customer Purchase ...

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Convergence in Engineering (ICCE), IEEE international Conference for

作者： Subhatav Dhali Monalisha Pati Soumi Ghosh Chandan Banerjee Netaji Subhash Engineering College Kolkata India

ISBN: (数字)9781728173405

ISBN: (纸本)9781728173412

Predictive Analytics is a bough of the advanced analytics which has been used to make predictions about unknown future events. It uses many techniques from data mining and statistics Modelling to analyze the current data. In statistics Modeling, Regression Analysis algorithms are some of the most popular processes used in machine learning Models. Random Forest is a supervised learning algorithm which uses Ensemble learning method to take advantage of Bootstrap Aggregating. XGBoost is a scalable & accurate implementation of Gradient Boosting machines (GBMs). It has been proved to push the limits of computing power. It is built & developed for the sole purpose of model performance and computational speed. Customers are the basis for growth of any type of business. In the study of sales and purchase, it is vital & crucial to be able to predict the amount of purchase or sales to increase benefit by catering from specific products to specific demographics. Our prediction analysis model can effectively help to improve the performance and increase the profit margin. Moreover, it can generalize the prediction of purchase or sales figures in any market which depends on the customers' past purchase pattern or behavior.

关键词： Predictive models Prediction algorithms machine learning algorithms Random forests Analytical models Training data models

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Pruning Extreme Wavelets learning machine by Automatic Relevance Determination 20th

Pruning Extreme Wavelets Learning Machine by Automatic Relev...

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： de Campos Souza, Paulo V. Silva Araujo, Vinicius J. Araujo, Vanessa S. Batista, Lucas O. Guimaraes, Augusto J. CEFET MG Ave Amazonas5253Nova Suica Belo Horizonte MG Brazil Fac UNA Betim Ave Gov Valadares640 Ctr Betim MG Brazil

ISBN: (纸本)9783030202576;9783030202569

Extreme learning machines are used for various contexts in artificial intelligence, such as for classifying patterns, performing time series prediction and regression problems, and being a more viable solution for training hidden layer weights to determine values of the learning model. However, the essence, the model determines that these weights should be determined randomly, and the Moore Penrose pseudoinverse will define only the weights that will act in the output layer. Random weights make this learning a black box because there is no relationship between the hidden layer weights and the problem data. This paper proposes the initialization of weights and bias in the hidden layer through the Wavelets transform that allows the two parameters, previously initialized at random, to be more representative about the problem domain, allowing the frequency range of the input patterns of the network to aid in the definition of weights of the ELM hidden layer. To assist in the representativeness of the data, a technique of selection of characteristics based on automatic relevance determination will be applied to the selection of the most characteristic dimensions of the problem. To compose the network structure, activation functions of the type rectified linear unit, also called ReLU, were used. The proposed model was submitted to the classification test of binary patterns in real classes, and the results show that the proposition of this work assists in bringing better accuracy to the classification results, and thus can be considered a feasible proposition to the training of neural networks that use extreme learning machine.

关键词： Extreme learning machine Wavelets ReLU Automatic relevance determination pattern classification

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