The Experts below are selected from a list of 295437 Experts worldwide ranked by ideXlab platform
Isaac S Kohane - One of the best experts on this subject based on the ideXlab platform.
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reproducible machine learning methods for lung cancer detection using computed tomography images Algorithm Development and validation
Journal of Medical Internet Research, 2020Co-Authors: Tsung Lu Michael Lee, Ming Hsuan Yen, S C Kou, Bruce R Rosen, Junghsien Chiang, Isaac S KohaneAbstract:Background Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely compared or reproduced. Objective The goal of the research was to generate reproducible machine learning modules for lung cancer detection and compare the approaches and performances of the award-winning Algorithms developed in the Kaggle Data Science Bowl. Methods We obtained the source codes of all award-winning solutions of the Kaggle Data Science Bowl Challenge, where participants developed automated CT evaluation methods to detect lung cancer (training set n=1397, public test set n=198, final test set n=506). The performance of the Algorithms was evaluated by the log-loss function, and the Spearman correlation coefficient of the performance in the public and final test sets was computed. Results Most solutions implemented distinct image preprocessing, segmentation, and classification modules. Variants of U-Net, VGGNet, and residual net were commonly used in nodule segmentation, and transfer learning was used in most of the classification Algorithms. Substantial performance variations in the public and final test sets were observed (Spearman correlation coefficient = .39 among the top 10 teams). To ensure the reproducibility of results, we generated a Docker container for each of the top solutions. Conclusions We compared the award-winning Algorithms for lung cancer detection and generated reproducible Docker images for the top solutions. Although convolutional neural networks achieved decent accuracy, there is plenty of room for improvement regarding model generalizability.
Evaristo C Biscaia - One of the best experts on this subject based on the ideXlab platform.
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genetic Algorithm Development for multi objective optimization of batch free radical polymerization reactors
Computers & Chemical Engineering, 2003Co-Authors: Claudia Lucia Martins Da Silva, Evaristo C BiscaiaAbstract:An improved genetic Algorithm approach, based on a new ranking strategy, has been proposed to conduct multi-objective optimization of chemical engineering problems. New operators have been introduced to enhance the Algorithm performance and reduce the computational effort. A Pareto-set filter operator has been implemented to avoid missing Pareto optimal points during the evolutionary process. A niche operator has been adopted to prevent genetic drift, and an elitism operator, to insure the propagation of the best result of each objective function. A fitness function based on each rank population size and rank level has been used to determine the reproduction ratio. Constraints are handled through a fuzzy penalty function method. The Algorithm has been applied to a batch free-radical styrene polymerization process in order to maximize the monomer conversion rate and minimize the concentration of initiator residue in the product. The Algorithm proved to be robust, handling satisfactorily multi-modal and multidimensional problems.
Jeffrey M Hausdorff - One of the best experts on this subject based on the ideXlab platform.
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automated detection of near falls Algorithm Development and preliminary results
BMC Research Notes, 2010Co-Authors: Aner Weiss, Ilan Shimkin, Nir Giladi, Jeffrey M HausdorffAbstract:Background: Falls are a major source of morbidity and mortality among older adults. Unfortunately, self-report is, to a large degree, the gold-standard method for characterizing and quantifying fall frequency. A number of studies have demonstrated that near falls predict falls and that near falls may occur more frequently than falls. These studies suggest that near falls might be an appropriate fall risk measure. However, to date, such investigations have also relied on self-report. The purpose of the present study was to develop a method for automatic detection of near falls, potentially a sensitive, objectivemarker of fall risk and to demonstrate the ability to detect near falls using this approach. Findings: 15 healthy subjects wore a tri-axial accelerometer on the pelvis as they walked on a treadmill under different conditions. Near falls were induced by placing obstacles on the treadmill and were defined using observational analysis. Acceleration-derived parameters were examined as potential indicators of near falls, alone and in various combinations. 21 near falls were observed and compared to 668 “non-near falls” segments, consisting of normal and abnormal (but not near falls) gait. The best single method was based on the maximum peak-to-peak vertical acceleration derivative, with detection rates better than 85% sensitivity and specificity. Conclusions: These findings suggest that tri-axial accelerometers may be used to successfully distinguish near falls from other gait patterns observed in the gait laboratory and may have the potential for improving the objective evaluation of fall risk, perhaps both in the lab and in at home-settings.
Ali H Sayed - One of the best experts on this subject based on the ideXlab platform.
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exact diffusion for distributed optimization and learning part i Algorithm Development
IEEE Transactions on Signal Processing, 2019Co-Authors: Kun Yuan, Bicheng Ying, Xiaochuan Zhao, Ali H SayedAbstract:This paper develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II of this paper to have a wider stability range and superior convergence performance than the EXTRA strategy. The exact diffusion method is applicable to locally balanced left-stochastic combination matrices which, compared to the conventional doubly stochastic matrix, are more general and able to endow the Algorithm with faster convergence rates, more flexible step-size choices, and improved privacy-preserving properties. The derivation of the exact diffusion strategy relies on reformulating the aggregate optimization problem as a penalized problem and resorting to a diagonally weighted incremental construction. Detailed stability and convergence analyses are pursued in Part II of this paper and are facilitated by examining the evolution of the error dynamics in a transformed domain. Numerical simulations illustrate the theoretical conclusions.
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exact diffusion for distributed optimization and learning part i Algorithm Development
arXiv: Optimization and Control, 2017Co-Authors: Kun Yuan, Bicheng Ying, Xiaochuan Zhao, Ali H SayedAbstract:This work develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II to have a wider stability range and superior convergence performance than the EXTRA strategy. The exact diffusion solution is applicable to non-symmetric left-stochastic combination matrices, while many earlier Developments on exact consensus implementations are limited to doubly-stochastic matrices; these latter matrices impose stringent constraints on the network topology. The derivation of the exact diffusion strategy in this work relies on reformulating the aggregate optimization problem as a penalized problem and resorting to a diagonally-weighted incremental construction. Detailed stability and convergence analyses are pursued in Part II and are facilitated by examining the evolution of the error dynamics in a transformed domain. Numerical simulations illustrate the theoretical conclusions.
Sairam Geethanath - One of the best experts on this subject based on the ideXlab platform.
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pulseq graphical programming interface open source visual environment for prototyping pulse sequences and integrated magnetic resonance imaging Algorithm Development
Magnetic Resonance Imaging, 2018Co-Authors: Keerthi Sravan Ravi, Pavan Poojar, Ramesh Venkatesan, Sneha Potdar, Jonfredrik Nielsen, Maxim Zaitsev, Ashok Kumar Reddy, Stefan Kroboth, Sairam GeethanathAbstract:Abstract Purpose To provide a single open-source platform for comprehensive MR Algorithm Development inclusive of simulations, pulse sequence design and deployment, reconstruction, and image analysis. Methods We integrated the “Pulseq” platform for vendor-independent pulse programming with Graphical Programming Interface (GPI), a scientific Development environment based on Python. Our integrated platform, Pulseq-GPI, permits sequences to be defined visually and exported to the Pulseq file format for execution on an MR scanner. For comparison, Pulseq files using either MATLAB only (“MATLAB-Pulseq”) or Python only (“Python-Pulseq”) were generated. We demonstrated three fundamental sequences on a 1.5 T scanner. Execution times of the three variants of implementation were compared on two operating systems. Results In vitro phantom images indicate equivalence with the vendor supplied implementations and MATLAB-Pulseq. The examples demonstrated in this work illustrate the unifying capability of Pulseq-GPI. The execution times of all the three implementations were fast (a few seconds). The software is capable of user-interface based Development and/or command line programming. Conclusion The tool demonstrated here, Pulseq-GPI, integrates the open-source simulation, reconstruction and analysis capabilities of GPI Lab with the pulse sequence design and deployment features of Pulseq. Current and future work includes providing an ISMRMRD interface and incorporating Specific Absorption Ratio and Peripheral Nerve Stimulation computations.