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Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention 1st

Automatic Detection of ECG Abnormalities by Using an Ensembl...

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1st international Workshop on Machine learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Liu, Yang He, Runnan Wang, Kuanquan Li, Qince Sun, Qiang Zhao, Na Zhang, Henggui Harbin150001 China School of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom SPACEnter Space Science and Technology Institute Shenzhen518117 China International Laboratory for Smart Systems and Key Laboratory of Intelligent of Computing in Medical Image Ministry of Education Northeastern University Shenyang110004 China The Department of Pharmacology Beijing Electric Power Hospital Beijing100073 China

ISBN: (纸本)9783030333263

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to suppress overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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data augmentation based on substituting regional MRIs volume scores 4th

Data augmentation based on substituting regional MRIs volume...

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4th international Workshop on Large-Scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2019, the 1st international Workshop on Hardware Aware learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the 2nd international Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd international conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019

作者： Leng, Tuo Zhao, Qingyu Yang, Chao Lu, Zhufu Adeli, Ehsan Pohl, Kilian M. School of Computer Engineering and Sciences Shanghai University Shanghai China School of Medicine Stanford University StanfordCA United States SRI International Center for Health Sciences Menlo ParkCA United States

ISBN: (纸本)9783030336417

Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible solution to the limited-data issue is to augment the training set with synthetically generated data. In this paper, we propose a data augmentation strategy based on regional feature substitution. We demonstrate the advantages of this strategy with respect to training a simple neural-network-based classifier in predicting when individual youth transition from no-to-low to medium-to-heavy alcohol drinkers solely based on their volumetric MRI measurements. Based on 20-fold cross-validation, we generate more than one million synthetic samples from less than 500 subjects for each training run. The classifier achieves an accuracy of 74.1% in correctly distinguishing non-drinkers from drinkers at baseline and a 43.2% weighted accuracy in predicting the transition over a three year period (5-group classification task). Both accuracy scores are significantly better than training the classifier on the original dataset. © Springer Nature Switzerland AG 2019.

关键词： Magnetic resonance imaging

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Intracranial Aneurysm Detection from 3D Vascular Mesh Models with Ensemble Deep learning 22nd

Intracranial Aneurysm Detection from 3D Vascular Mesh Models...

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhou, Mingsong Wang, Xingce Wu, Zhongke Pozo, Jose M. Frangi, Alejandro F. Beijing Normal Univ Informat Sci & Technol Coll Beijing Peoples R China Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Sch Comp Leeds W Yorkshire England Univ Leeds Sch Med Leeds W Yorkshire England

ISBN: (纸本)9783030322519;9783030322502

Intracranial aneurysm rupture can cause a serious stroke, which is related to the decline of daily life ability of the elderly. Although deep learning is now the most successful solution for organ detection, it requires myriads of training data, consistent of the image format, and a balanced sample distribution. This work presents an innovative representation of intracranial aneurysm detection as a shape analysis problem rather than a computer vision problem. We detected intracranial aneurysms in 3D cerebrovascular mesh models after segmentation of the brain vessels from the medical images, which can overcome the barriers of data format and data distribution, serving both clinical and screening purposes. Additionally, we propose a transferable multi-model ensemble (MMEN) architecture to detect intracranial aneurysms from cerebrovascular mesh models with limited data. To obtain a well-defined convolution operator, we use a global seamless parameterization converting a 3D cerebrovascular mesh model to a planar flat-torus. In the architecture, we transfer the planar flat-torus presentation abilities of three GoogleNet Inception V3 models, which were pre-trained on the ImageNet database, to characterize the intracranial aneurysms with local and global geometric features such as Gaussian curvature (GC), shape diameter function (SDF) and wave kernel signature (WKS), respectively. We jointly utilize all three models to detect aneurysms with adaptive weights learning based on back propagation. The experimental results on the 121 models show that our proposed method can achieve detection accuracy of 95.1% with 94.7% F1-score and 94.8% sensitivity, which is as good as the state-of-art work but is applicable to inhomogeneous image modalities and smaller datasets.

关键词： Backpropagation

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Unsupervised feature selection via adaptive embedding and sparse learning for Parkinson’s disease diagnosis 1

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3rd international Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

作者： Huang, Zhongwei Lei, Haijun Chen, Guoliang Li, Shiqi Li, Hancong Elazab, Ahmed Lei, Baiying Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China

ISBN: (数字)9783030323912

ISBN: (纸本)9783030323905

Parkinson’s disease (PD) is known as a progressive neurodegenerative disease in elderly people. Apart from decelerating the disease exacerbation, early and accurate diagnosis also alleviates mental and physical sufferings and provides timely and appropriate medication. In this paper, we propose an unsupervised feature selection method via adaptive manifold embedding and sparse learning exploiting longitudinal multimodal neuroimaging data for classification and regression prediction. Specifically, the proposed method simultaneously carries out feature selection and dynamic local structure learning to obtain the structural information inherent in the neuroimaging data. We conduct extensive experiments on the publicly available Parkinson’s progression markers initiative (PPMI) dataset to validate the proposed method. Our proposed method outperforms other state-of-the-art methods in terms of classification and regression prediction performance. © Springer Nature Switzerland AG 2019.

关键词： Neuroimaging

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Modelling Airway Geometry as Stock Market data Using Bayesian Changepoint Detection 10th

Modelling Airway Geometry as Stock Market Data Using Bayesia...

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Quan, Kin Tanno, Ryutaro Duong, Michael Nair, Arjun Shipley, Rebecca Jones, Mark Brereton, Christopher Hurst, John Hawkes, David Jacob, Joseph UCL Ctr Med Image Comp London England UCL Stat Sci London England Univ Coll Hosp Dept Radiol London England UCL Mech Engn London England Univ Southampton NIHR Biomed Res Ctr Southampton Hants England UCL UCL Resp London England

ISBN: (纸本)9783030326920;9783030326913

Numerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exhibit dilation of the airways. Accurate measurement of dilatation enables assessment of the progression of disease. Unfortunately the combination of image noise and airway bifurcations causes high variability in the profiles of cross-sectional areas, rendering the identification of affected regions very difficult. Here we introduce a noise-robust method for automatically detecting the location of progressive airway dilatation given two profiles of the same airway acquired at different time points. We propose a probabilistic model of abrupt relative variations between profiles and perform inference via Reversible Jump Markov Chain Monte Carlo sampling. We demonstrate the efficacy of the proposed method on two datasets;(i) images of healthy airways with simulated dilatation;(ii) pairs of real images of IPF-affected airways acquired at 1 year intervals. Our model is able to detect the starting location of airway dilatation with an accuracy of 2.5mm on simulated data. The experiments on the IPF dataset display reasonable agreement with radiologists. We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.

关键词： Monte Carlo methods

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Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set 4th

Landmark-based evaluation of a block-matching registration f...

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作者： Drobny, David Ranzini, Marta Ourselin, Sébastien Vercauteren, Tom Modat, Marc Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London LondonWC1E 6BT United Kingdom Medical Physics and Biomedical Engineering Department University College London LondonWC1E 6BT United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London King’s Health Partners St Thomas’ Hospital LondonSE1 7EH United Kingdom

ISBN: (纸本)9783030336417

In this paper, we describe the application of an established block-matching based registration method to the CuRIOUS 2019 MICCAI registration challenge. Directional and symmetric approaches with different parameters are evaluated to select the most suitable setting of this fully automatic and general registration method. The results can be used as a baseline, for example when evaluating methods specialised in ultrasound (US) to MRI registration or registration of different interventional US (iUS) data. This work is a continuation of our contribution to the CuRIOUS 2018 challenge. We provide a more extensive analysis of main parameters as well as add pre- to post-resection iUS registration to the previous MRI-iUS registration. The proposed approach achieves an average target registration error of 2.68 mm and 1.92 mm for the MR-iUS and the iUS-iUS task respectively. © Springer Nature Switzerland AG 2019.

关键词： Magnetic resonance imaging

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Proposal of effective preprocessing techniques of financial data 22

Proposal of effective preprocessing techniques of financial ...

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22nd IEEE international conference on intelligent engineering Systems (INES)

作者： Abasova, Jela Janosik, Jan Simoncicova, Veronika Tanuska, Pavol Slovak Univ Technol Bratislava Fac Mat Sci & Technol Trnava Trnava Slovakia

ISBN: (纸本)9781538611227

The article focuses on describing process of customization data obtained from ERP software. The aim of this customization was ensuring usability of data collection for obtaining knowledge from data via methods of data mining. First part describes importance of preprocessing phase of working with big data sets. It deals with terms commonly used in the topic - methodology CRISP-DM and its phases, with closer look at individual steps of preprocessing. Second part contains description of the chosen dataset, extraction of raw data and their required format (according to their expected future use). The data are financial indications of a real concern. This part also focuses on methods and programs used for preprocessing the data. Third part corresponds with the main task of this article, what is adjusting obtained data to format which would be appropriate to further processing and analyzing purposes with data Mining techniques. It describes five phases of preprocessing, modified in a way needed for manipulating with chosen data. The last part compiles acquired knowledge and presents further use of the data after transformation from raw form to demanded format. The paper concludes with BPMN diagram of the process with focusing on preprocessing (data preparation) stage.

关键词： preprocessing Big data data Mining BPMN diagram CRISP-DM

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Automatic Multi-label Classification in 12-Lead ECGs Using Neural Networks and Characteristic Points 1st

Automatic Multi-label Classification in 12-Lead ECGs Using ...

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作者： Xia, Zhourui Sang, Zhenhua Guo, Yutong Ji, Weijie Han, Chenguang Chen, Yanlin Yang, Sifan Meng, Long Shenzhen International Graduate School Tsinghua University Shenzhen China Beijing Tsinghua Changgung Hospital Beijing China School of Information and Electronics Beijing Institute Technology Beijing China Shandong Mingjia Technology Co. Ltd. Tai’an China

ISBN: (纸本)9783030333263

Electrocardiogram (ECG) signals are widely used in the medical diagnosis of heart disease. Automatic extraction of relevant and reliable information from ECG signals is a tough challenge for computer systems. This study proposes a novel 12-lead electrocardiogram (ECG) multi-label classification algorithm using a combination of Neural Network (NN) and the characteristic points. The proposed model is an end-to-end model. CNN extracts the morphological features of each ECG. Then the features of all the beats are considered in the context via BiRNN. The proposed method was evaluated on the dataset offered by The First China intelligent Competition, and results were measured using the macro F1 score of all nine classes. Our proposed method obtained a macro F1 score of 0.878, which is excellent among the competitors. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Arrhythmia Classification with Attention-Based Res-BiLSTM-Net 1st

Arrhythmia Classification with Attention-Based Res-BiLSTM-Ne...

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作者： Huang, Chengbin Zhao, Renjie Chen, Weiting Li, Huazheng MOE Engineering Research Center for Hardware/Software Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9783030333263

In the modern clinical diagnosis, the 12-lead electrocardiogram (ECG) signal has proved effective in cardiac arrhythmias classification. However, the manual diagnosis for cardiac arrhythmias is tedious and error-prone through ECG signals. In this work, we propose an end-to-end deep neural network called attention-based Res-BiLSTM-Net for automatic diagnosis of cardiac arrhythmias. Our model is capable of classifying ECG signals with different lengths. The proposed network consists of two parts: the attention-based Resnet and the attention-based BiLSTM. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by our attention-based Resnet through signal segments. Next, these multi-scale features from a same ECG signal are integrated in chronological order. In the end, our attention-based BiLSTM classifies cardiac arrhythmias according to combined features. Our method achieved a good result with an average F1score of 0.8757 on a multi-label arrhythmias classification problem in the First China ECG intelligent Competition. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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intelligent Production Optimization Decisions: Prioritizing Production Optimization Through Machine learning Aided Uplift Quantification

Intelligent Production Optimization Decisions: Prioritizing ...

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Abu Dhabi international Petroleum Exhibition and conference 2022, ADIPEC 2022

作者： Sharma, Akriti Ahn, Alex Chonghyun Park, Jihoon Singh, Vikash Rodriguez-Buno, Mariana El-Bakry, Amr Kwong, John Garrison, Brett Gelowitz, Tyler ExxonMobil Imperial Oil Limited

ISBN: (纸本)9781613998724

Due to complexity and scale, managing and engineering a modern hydrocarbon asset is extremely challenging. The problem becomes more difficult if an asset has thousands of wells to manage because, most of the time, operation resource is limited (either manpower or tool) to achieve production optimization and the amount of information to process during decision making is enormous. Bringing necessary data from multiple different sources and analyzing it in a relatively short period of time to provide actionable/reliable insight to field operators' and engineers' is the key to enhance and optimize production performance. The production optimization best practice is shared amongst field operators and engineers given the geology, reservoir character, and operation strategy which include artificial lift, stimulation, and other workover. This approach has been applied and proven successful mostly in low well count assets by identifying opportunity and providing robust qualitative guidance towards optimum production condition. However, in high well count assets such as unconventional, it is impossible to execute traditional manual approach and it is essential to develop an integrated, flexible, and automated workflow that enhances the efficiency and effectiveness of production operation and achieve "operate-by-priority". Thus, to have efficient and optimized management of hydrocarbon production from individual well of a high well count asset requires prior, more certain and almost real time knowledge of production rates from the currently or upcoming production wells. Production data, thus, from individual well is very important. Well tests are carried out to obtain production rates and then utilize these rates to provide optimized production decisions and actions. In a well test, a test separator splits the production stream into oil, gas, and water using gravity and support in the management of hydrocarbon production. However, well test are infrequent and doesn't fit the p

关键词： Decision making

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