Resolving intravoxel white matter structures in the human brain using regularized regression and clustering

Hart, Andrea; Smith, Brianna; Smith, S. M.; Sales, Elijah; Hernandez-Camargo, Jacqueline; Garcia, Yarlin Mayor; Zhan, F. Benjamin; Griswold, Lori; Dunkelberger, Brian; Schwob, Michael R.; Chaudhry, Sharang; Zhan, Justin; Gewali, Laxmi; Oh, Paul

doi:10.1186/s40537-019-0223-2

Cited by 11 publications

(1 citation statement)

References 31 publications

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“…Hart, et al [18] applied machine learning technique to estimate intra-voxel nerve number and direction from diffusion Magnetic Resonance Imaging (dMRI). The synthetic data were used to evaluate the performance of the method that shows the developed method has the higher performance.…”

Section: Analysis On Big Data Image Processing On Medical Applicationmentioning

confidence: 99%

A Systematic Analysis of Big Image Data Methodologies in Various Applications

Nayak*,

2020

IJITEE

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Big data is large-scale data collected for knowledge discovery, it has been widely used in various applications. Big data often has image data from the various applications and requires effective technique to process data. In this paper, survey has been done in the big image data researches to analysis the effective performance of the methods. Deep learning techniques provides the effective performance compared to other methods included wavelet based methods. The deep learning techniques has the problem of requiring more computational time, and this can be overcome by lightweight methods.

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Section: Analysis On Big Data Image Processing On Medical Applicationmentioning

confidence: 99%

A Systematic Analysis of Big Image Data Methodologies in Various Applications

Nayak*,

2020

IJITEE

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An Evolutionary Approach to Compact DAG Neural Network Optimization

Chiu

Zhan

2019

IEEE Access

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Neural networks are the cutting edge of artificial intelligence, demonstrated to reliably outperform other techniques in machine learning. Within the domain of neural networks, many different classes of architectures have been developed for various tasks in specific subfields, as well as a multitude of diversity in the way of activation functions, loss functions, and other such hyperparameters. These networks are often large and computationally expensive to train and deploy, restricting their utility. Furthermore, the fundamental theory behind the effectiveness of particular network architectures and hyperparameters are often not well understood, and as such, practitioners frequently resort to trial-and-error techniques to optimize their model performance. To address these concerns, we propose the use of compact directed acyclic graph neural networks (DAG-NNs) and an evolutionary approach for automating the optimization of their structure and parameters. Our experimental results demonstrate that our approach consistently outperforms conventional neural networks, even while employing fewer nodes. INDEX TERMS Evolutionary algorithms, DAG neural networks, compact neural networks, artificial intelligence.

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Deep Physical Informed Neural Networks for Metamaterial Design

2020

Self Cite

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In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as cloaking, rotators, concentrators, etc. The advantage of this approach is the flexibility that we can deal with not only the continuous parameters but also the piecewise constants. As our best knowledge, there is no other faster and much efficient method to deal with these problems. As a byproduct, we propose a method to solve high frequency Helmholtz equation, which is widely used in physics and engineering. Some benchmark problems have been solved in numerical tests to verify our method.

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Resolving intravoxel white matter structures in the human brain using regularized regression and clustering

Cited by 11 publications

References 31 publications

A Systematic Analysis of Big Image Data Methodologies in Various Applications

A Systematic Analysis of Big Image Data Methodologies in Various Applications

An Evolutionary Approach to Compact DAG Neural Network Optimization

Deep Physical Informed Neural Networks for Metamaterial Design

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