Segmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests

2016 ◽  
pp. 156-163 ◽  
Author(s):  
Mohammad Arafat Hussain ◽  
Ghassan Hamarneh ◽  
Timothy W. O’Connell ◽  
Mohammed F. Mohammed ◽  
Rafeef Abugharbieh
Keyword(s):  
Dual Regression

scholarly journals Dual regression

2016 ◽  
Author(s):  
Sami Stouli ◽  
Richard Spady
Keyword(s):  
Dual Regression

Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal

2021 ◽  
pp. 109410
Author(s):  
Robert E. Kelly, Jr. ◽  
Matthew J. Hoptman ◽  
Soojin Lee ◽  
George S. Alexopoulos ◽  
Faith M. Gunning ◽  
...  
Keyword(s):  
Global Signal ◽  
Dual Regression ◽  
The Impact

Super-Resolution Based on Generative Adversarial Network for HRTEM Images

2021 ◽  
pp. 2154027
Author(s):  
Fuqi Mao ◽  
Xiaohan Guan ◽  
Ruoyu Wang ◽  
Wen Yue
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Information Structure ◽  
Rapid Development ◽  
Super Resolution ◽  
Training Data ◽  
Learning Technology ◽  
Hrtem Image ◽  
Generative Adversarial Network ◽  
Dual Regression

As an important tool to study the microstructure and properties of materials, High Resolution Transmission Electron Microscope (HRTEM) images can obtain the lattice fringe image (reflecting the crystal plane spacing information), structure image and individual atom image (which reflects the configuration of atoms or atomic groups in crystal structure). Despite the rapid development of HTTEM devices, HRTEM images still have limited achievable resolution for human visual system. With the rapid development of deep learning technology in recent years, researchers are actively exploring the Super-resolution (SR) model based on deep learning, and the model has reached the current best level in various SR benchmarks. Using SR to reconstruct high-resolution HRTEM image is helpful to the material science research. However, there is one core issue that has not been resolved: most of these super-resolution methods require the training data to exist in pairs. In actual scenarios, especially for HRTEM images, there are no corresponding HR images. To reconstruct high quality HRTEM image, a novel Super-Resolution architecture for HRTEM images is proposed in this paper. Borrowing the idea from Dual Regression Networks (DRN), we introduce an additional dual regression structure to ESRGAN, by training the model with unpaired HRTEM images and paired nature images. Results of extensive benchmark experiments demonstrate that the proposed method achieves better performance than the most resent SISR methods with both quantitative and visual results.

Dual-regression model for visual tracking

2020 ◽  
Vol 132 ◽  
pp. 364-374 ◽  
Author(s):  
Xin Li ◽  
Qiao Liu ◽  
Nana Fan ◽  
Zikun Zhou ◽  
Zhenyu He ◽  
...  
Keyword(s):  
Regression Model ◽  
Visual Tracking ◽  
Dual Regression

Experimental analysis of critical oxygen delivery

2005 ◽  
Vol 288 (3) ◽  
pp. H1071-H1079 ◽  
Author(s):  
Ivo P. Torres Filho ◽  
Bruce D. Spiess ◽  
Roland N. Pittman ◽  
R. Wayne Barbee ◽  
Kevin R. Ward
Keyword(s):  
Cardiac Index ◽  
Multivariable Analysis ◽  
Systolic Pressure ◽  
Whole Body ◽  
Critical Levels ◽  
Dual Regression ◽  
Body Level ◽  
Multiple Variables ◽  
Systemic Variables ◽  
Stored Blood

Systemic variables were evaluated with respect to O2 delivery to test the hypothesis that critical O2 delivery and critical Hb can be estimated by multiple variables collected simultaneously. Rats were subjected to transfusion with either fresh or stored blood and then subjected to stepwise isovolemic hemodilution. Critical levels were measured by the dual-regression method from plots of systemic variables against O2 delivery and Hb. Delivery was calculated from cardiac index and arterial O2 content. We found that 1) after hemodilution, O2 delivery changed in a nonlinear relationship with Hb; 2) critical delivery calculated using 30 different systemic variables was not statistically different from each other; 3) critical delivery and critical Hb were correlated but were not different between animals receiving fresh or stored blood; and 4) similar critical levels were found using a single variable from several animals and using several variables from the same subject. The best variables to estimate critical delivery were lactate, bicarbonate, base excess, O2 extraction ratio, expired CO2, pulse pressure, cardiac index, and systolic pressure. The data suggest that a multivariable analysis of critical delivery may help determine the physiological oxygenation boundary at the whole body level. This may assist in finding therapeutic triggers on an individual basis using systemic markers of the transition from aerobic to anaerobic metabolism.

scholarly journals Human subsystems of medial temporal lobes extend locally to amygdala nuclei and globally to an allostatic-interoceptive system

2019 ◽  
Author(s):  
Adriana L. Ruiz-Rizzo ◽  
Florian Beissner ◽  
Kathrin Finke ◽  
Hermann J. Müller ◽  
Claus Zimmer ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Local Level ◽  
Resting State Fmri ◽  
Medial Temporal Lobes ◽  
Default Mode ◽  
Temporal Lobes ◽  
Dual Regression ◽  
Slow Activity ◽  
Global Brain

AbstractIn mammals, the hippocampus, entorhinal, perirhinal, and parahippocampal cortices (i.e., core regions of the human medial temporal lobes, MTL) are locally interlaced with the adjacent amygdala nuclei at the structural and functional levels. At the global brain level, the human MTL has been described as part of the default mode network whereas amygdala nuclei as parts of the salience network, with both networks forming collectively a large-scale brain system supporting allostatic-interoceptive functions. We hypothesized (i) that intrinsic functional connectivity of slow activity fluctuations would reveal human MTL subsystems locally extending to the amygdala; and (ii) that these extended local subsystems would be globally embedded in large-scale brain systems supporting allostatic-interoceptive functions. From the resting-state fMRI data of three independent samples of cognitively healthy adults (one main and two replication samples: Ns = 101, 61, and 29, respectively), we analyzed the functional connectivity of fluctuating ongoing BOLD-activity within and outside the amygdala-MTL in a data-driven way using masked independent component and dual-regression analyses. We found that at the local level MTL subsystems extend to the amygdala and are functionally organized along the longitudinal amygdala-MTL axis. These subsystems were characterized by a consistent involvement of amygdala, hippocampus, and entorhinal cortex, but a variable participation of perirhinal and parahippocampal regions. At the global level, amygdala-MTL subsystems selectively connected to salience, thalamic-brainstem, and default mode networks – the major cortical and subcortical parts of the allostatic-interoceptive system. These results provide evidence for integrated amygdala-MTL subsystems in humans, which are embedded within a larger allostatic-interoceptive system.

scholarly journals FACTORS WHICH INFLUENCE LIQUID ASSETS IN SHARIA BANKS: A CASE STUDY OF PT BANK SYARIAH MANDIRI 2016-2017

2020 ◽  
Vol 9 (2) ◽  
pp. 133
Author(s):  
Taryana Harun
Keyword(s):  
Financial Market ◽  
Market Access ◽  
Third Party ◽  
Liquidity Management ◽  
Reserve Requirement ◽  
Trade Off ◽  
Independent Variables ◽  
Dual Regression ◽  
Lost Opportunity

Banks manage liquidity carefully because of differences in fund tenor collected and channeled. Meanwhile, at the same time, it must fulfill transaction needs, reserve requirement, current liabilities, and be cautious in facing sudden liquidity needs. Therefore, bankshold a sufficient amount of liquid assets. Liquidity management tends to be a trade-off. On one side, insufficient liquid assets can cause banks to be unable to carry out transactions with its customers or fulfill its maturity obligations. On another side, high liquid assets can result in a lost opportunity, because the liquid assets do not provide a return. The purpose of this research is to analyze what factors influence the level of banks liquid assets. This research was conducted using a dual regression model to analyze the variables studied, with a case study of PT Bank Syariah Mandiri from 2016-2017.The dependent variable was the level of liquid assets. Meanwhile, the independent variables were the amount of third party funds, financing growth, financial market access between banks, current liabilities, and previous month profit. The research results reveal that two variables are statistically significant towards bank liquid assets, which are third-party funds and previous month profit. Third-party funds and previous month profit have a positive and significat influence towards liquid assets. Meanwhile, the other variables do not significantly determined liquid assets.

Group comparison of resting-state FMRI data using multi-subject ICA and dual regression

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S148 ◽  
Author(s):  
CF Beckmann ◽  
CE Mackay ◽  
N Filippini ◽  
SM Smith
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Group Comparison ◽  
Dual Regression
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