The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Anthony H Aletras - One of the best experts on this subject based on the ideXlab platform.
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simulator generated Training Datasets as an alternative to using patient data for machine learning an example in myocardial segmentation with mri
Computer Methods and Programs in Biomedicine, 2021Co-Authors: Christos G Xanthis, Kostas Haris, Dimitrios Filos, Anthony H AletrasAbstract:Abstract Background and Objective: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the Training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses. Methods: The 4D-XCAT model was used with the coreMRI simulation platform for generating artificial short-axis MR-images for Training a neural-network to automatic delineate the LV endocardium and epicardium. Its performance was assessed on real MR-images acquired from eight healthy volunteers. The neural-network was also trained on real MR-images from a publicly available dataset and its performance was assessed on the same volunteers’ data. Results: The proposed solution demonstrated a performance of 94% (endocardium) and 90% DICE (epicardium) in real mid-ventricular slices, whereas a 10% addition of real MR-images in the artificial Training dataset increased the performance to 97% DICE. The use of artificial MR-images that cover the entire LV yielded 85% (endocardium) and 88% DICE (epicardium) when combined with real MR data with an 80%-20% mix respectively. Conclusions: This study suggests a low-cost solution for constructing artificial Training Datasets for supervised learning techniques in the field of MR by using advanced MR simulations without the use of a real MRI scanner, without scanning patients and without having to use specialized personnel, such as technologists and radiologists.
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Artificially Generated Training Datasets for Supervised Machine Learning Techniques in Magnetic Resonance Imaging: An Example in Myocardial Segmentation
2019 Computing in Cardiology (CinC), 2019Co-Authors: Christos G Xanthis, Kostas Haris, Dimitrios Filos, Anthony H AletrasAbstract:Machine learning techniques have become increasingly successful over the last few years in medical image analysis and radiology. However, the low availability, relativeness and size of the Training data sets required by the associated learning algorithms prevents their further development or delays their application in clinical practice.This study presented for the first time the development of artificially generated Training Datasets for supervised learning techniques through the incorporation of a realistic simulation framework in the field of Magnetic Resonance Imaging (MRI). An example in left-ventricle segmentation was utilized and the performance of a fully convolutional network on true cardiac MR data was evaluated.
Christos G Xanthis - One of the best experts on this subject based on the ideXlab platform.
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simulator generated Training Datasets as an alternative to using patient data for machine learning an example in myocardial segmentation with mri
Computer Methods and Programs in Biomedicine, 2021Co-Authors: Christos G Xanthis, Kostas Haris, Dimitrios Filos, Anthony H AletrasAbstract:Abstract Background and Objective: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the Training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses. Methods: The 4D-XCAT model was used with the coreMRI simulation platform for generating artificial short-axis MR-images for Training a neural-network to automatic delineate the LV endocardium and epicardium. Its performance was assessed on real MR-images acquired from eight healthy volunteers. The neural-network was also trained on real MR-images from a publicly available dataset and its performance was assessed on the same volunteers’ data. Results: The proposed solution demonstrated a performance of 94% (endocardium) and 90% DICE (epicardium) in real mid-ventricular slices, whereas a 10% addition of real MR-images in the artificial Training dataset increased the performance to 97% DICE. The use of artificial MR-images that cover the entire LV yielded 85% (endocardium) and 88% DICE (epicardium) when combined with real MR data with an 80%-20% mix respectively. Conclusions: This study suggests a low-cost solution for constructing artificial Training Datasets for supervised learning techniques in the field of MR by using advanced MR simulations without the use of a real MRI scanner, without scanning patients and without having to use specialized personnel, such as technologists and radiologists.
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Artificially Generated Training Datasets for Supervised Machine Learning Techniques in Magnetic Resonance Imaging: An Example in Myocardial Segmentation
2019 Computing in Cardiology (CinC), 2019Co-Authors: Christos G Xanthis, Kostas Haris, Dimitrios Filos, Anthony H AletrasAbstract:Machine learning techniques have become increasingly successful over the last few years in medical image analysis and radiology. However, the low availability, relativeness and size of the Training data sets required by the associated learning algorithms prevents their further development or delays their application in clinical practice.This study presented for the first time the development of artificially generated Training Datasets for supervised learning techniques through the incorporation of a realistic simulation framework in the field of Magnetic Resonance Imaging (MRI). An example in left-ventricle segmentation was utilized and the performance of a fully convolutional network on true cardiac MR data was evaluated.
Bruce Fischl - One of the best experts on this subject based on the ideXlab platform.
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unsupervised medical image segmentation based on the local center of mass
Scientific Reports, 2018Co-Authors: Iman Aganj, Mukesh G Harisinghani, Ralph Weissleder, Bruce FischlAbstract:Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large Training Datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary, can be used in the absence of Training data to segment new images. We introduce a new approach to unsupervised image segmentation that is based on the computation of the local center of mass. We propose an efficient method to group the pixels of a one-dimensional signal, which we then use in an iterative algorithm for two- and three-dimensional image segmentation. We validate our method on a 2D X-ray image, a 3D abdominal magnetic resonance (MR) image and a dataset of 3D cardiovascular MR images.
S Ben J Yoo - One of the best experts on this subject based on the ideXlab platform.
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experimental demonstration of machine learning aided qot estimation in multi domain elastic optical networks with alien wavelengths
IEEE\ OSA Journal of Optical Communications and Networking, 2019Co-Authors: Roberto Proietti, Xiaoliang Chen, Kaiqi Zhang, Gengchen Liu, M Shamsabardeh, Alberto Castro, Luis Velasco, Zuqing Zhu, S Ben J YooAbstract:In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/ interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength light- path. By using experimental Training Datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ~95% when using 1200 data points.
Mohd Zakree Ahmad Nazri - One of the best experts on this subject based on the ideXlab platform.
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multi level hybrid support vector machine and extreme learning machine based on modified k means for intrusion detection system
Expert Systems With Applications, 2017Co-Authors: Wathiq Laftah Alyaseen, Zulaiha Ali Othman, Mohd Zakree Ahmad NazriAbstract:Reduction the 10%KDD Training dataset up to 99.8% by using modified K-means.New high quality Training Datasets are constructed for Training SVM and ELM.Multi-level model is proposed to improve the performance of detection accuracy.Improve the detection rate of DoS, U2R and R2L attacks.Overall accuracy of 95.75% is achieved with whole Corrected KDD dataset. Intrusion detection has become essential to network security because of the increasing connectivity between computers. Several intrusion detection systems have been developed to protect networks using different statistical methods and machine learning techniques. This study aims to design a model that deals with real intrusion detection problems in data analysis and classify network data into normal and abnormal behaviors. This study proposes a multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks. A modified K-means algorithm is also proposed to build a high-quality Training dataset that contributes significantly to improving the performance of classifiers. The modified K-means is used to build new small Training Datasets representing the entire original Training dataset, significantly reduce the Training time of classifiers, and improve the performance of intrusion detection system. The popular KDD Cup 1999 dataset is used to evaluate the proposed model. Compared with other methods based on the same dataset, the proposed model shows high efficiency in attack detection, and its accuracy (95.75%) is the best performance thus far.
