scholarly journals Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1177
Author(s):  
Yanhua Gao ◽  
Yuan Zhu ◽  
Bo Liu ◽  
Yue Hu ◽  
Gang Yu ◽  
...  
Keyword(s):  
Cardiac Cycle ◽  
Test Set ◽  
Automated Recognition ◽  
Cardiac Image ◽  
Independent Test ◽  
Comparable Accuracy ◽  
The Mean ◽  
Shape Changes ◽  
Generalization Accuracy ◽  
Better Than

In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac views is proposed consisting of three processes. First, a spatial transform network is performed to learn cardiac shape changes during a cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrate channel-wise feature responses. Finally, the structured signals by the similarities among cardiac views are transformed into the graph-based image embedding, which acts as unsupervised regularization constraints to improve the generalization accuracy. The proposed method is trained and tested in 171792 cardiac images from 584 subjects. The overall accuracy of the proposed method on cardiac image classification is 99.10%, and the mean AUC is 99.36%, better than known methods. Moreover, the overall accuracy is 97.73%, and the mean AUC is 98.59% on an independent test set with 37,883 images from 100 subjects. The proposed automated recognition model achieved comparable accuracy with true cardiac views, and thus can be applied clinically to help find standard cardiac views.

scholarly journals Automated recognition of ultrasound cardiac views based on deep learning with graph constraint

2020 ◽  
Author(s):  
Yanhua Gao ◽  
Yuan Zhu ◽  
Bo Liu ◽  
Yue Hu ◽  
Youmin Guo
Keyword(s):  
Deep Learning ◽  
Cardiac Cycle ◽  
State Of The Art ◽  
The State ◽  
Automated Recognition ◽  
Cardiac Image ◽  
Independent Test ◽  
The Mean ◽  
Shape Changes ◽  
Generalization Accuracy

ObjectiveIn Transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of the TTE examination.MethodsThis paper proposes a new method for automatic recognition of cardiac views based on deep learning, including three strategies. First, A spatial transform network is performed to learn cardiac shape changes during the cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrates channel-wise feature responses. Finally, unlike conventional deep learning methods, which learned each input images individually, the structured signals are applied by a graph of similarities among images. These signals are transformed into the graph-based image embedding, which act as unsupervised regularization constraints to improve the generalization accuracy.ResultsThe proposed method was trained and tested in 171792 cardiac images from 584 subjects. Compared with the known result of the state of the art, the overall accuracy of the proposed method on cardiac image classification is 99.10% vs. 91.7%, and the mean AUC is 99.36%. Moreover, the overall accuracy is 98.15%, and the mean AUC is 98.96% on an independent test set with 34211 images from 100 subjects.ConclusionThe method of this paper achieved the results of the state of the art, which is expected to be an automated recognition tool for cardiac views recognition. The work confirms the potential of deep learning on ultrasound medicine.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

Quantitative analysis of static equilibrium in women after mastectomy

2014 ◽  
Vol 14 (1) ◽  
pp. 81-87
Author(s):  
Maciej Rachwał ◽  
Justyna Drzał-Grabiec ◽  
Katarzyna Walicka-Cupryś ◽  
Aleksandra Truszczyńska
Keyword(s):  
Mean Amplitude ◽  
Control Group ◽  
Foot Pressure ◽  
Locomotor System ◽  
Center Of Foot Pressure ◽  
The Mean ◽  
Almost All ◽  
Posture Stability ◽  
Mastectomy Group ◽  
Better Than

Abstract Background: The post-mastectomy changes to the locomotor system are related to the scar and adhesion or to the lymphatic edema after amputation which, in turn, lead to local and global distraction of the work of the muscles. These changes lead to body statics disturbance that changes the projection of the center of gravity and worsens motor response due to changing of the muscle sensitivity. Objective: The aim of the study was to evaluate the static balance of women after undergoing mastectomy. Methods: The study included 150 women, including 75 who underwent mastectomy (mean age: 60±7.6) years, mean body mass index (BMI): 26 (±3.6) kg/m2) and 75 who were placed in the control group with matched age and BMI. The study was conducted using a tensometric platform. Results: Statistically significant differences were found for almost all parameters between the post-mastectomy group and group of healthy women, regarding center of foot pressure (COP) path length in the Y and X axes and the mean amplitude of COP. Conclusions: First, the findings revealed that balance in post-mastectomy women is significantly better than in the control group. Second, physiotherapeutic treatment of post-mastectomy women may have improved their posture stability compared with their peers.

scholarly journals Heterobeltiosis in French Marigold (Tagetes patula L.)

2016 ◽  
Vol 8 (8) ◽  
pp. 182
Author(s):  
Kanwar Priyanaka ◽  
Y. C. Gupta ◽  
S. R. Dhiman ◽  
R. K. Dogra ◽  
Sharma Madhu ◽  
...  
Keyword(s):  
Rainy Season ◽  
Quantitative Traits ◽  
Block Design ◽  
Tagetes Patula ◽  
Male Sterile ◽  
Randomized Block Design ◽  
Parental Material ◽  
The Mean ◽  
Parental Lines ◽  
Better Than

<p>The studies on heterosis were carried with four male sterile lines namely; ms<sub>7</sub>, ms<sub>8</sub>, ms<sub>9,</sub> ms<sub>10</sub> and 18 diverse pollinators as tester by using line × tester crossing programme. The 72 F<sub>1</sub> hybrids were produced and evaluated along with 22 parental lines during summer 2009 and rainy season 2009 in Randomized Block Design. Observations were recorded on nine quantitative traits during both the seasons. Highly significant variances for all the traits indicated the sufficient variability in the parental material for all the characters under study. The performance of F<sub>1</sub> hybrids was much better than the mean performance of parents during both the crop seasons. Appreciable heterosis was observed in all the characters, except flower weight in summer and plant height in rainy season.</p>

A Comparison of Two Aerial Photo Volume Tables for Pine Stands in Central Mississippi

1981 ◽  
Vol 5 (2) ◽  
pp. 92-96 ◽  
Author(s):  
James L. Smith ◽  
Roy A. Mead
Keyword(s):  
Prediction Models ◽  
Quadratic Model ◽  
Aerial Photo ◽  
Test Set ◽  
Volume Table ◽  
Independent Test ◽  
Pine Stands ◽  
Negative Volume

Abstract Two aerial photo volume prediction models, Avery's Composite Aerial Volume Table, and Mead's Quadratic Model, were compared using graphs and a small independent test set. The graphs indicated that Mead's model predicted higher merchantable volumes for pine stands in central Mississippi than did Avery's model. Both models tended to underpredict ground merchantable volume. However, only Avery's model underpredicted in a statistically significant manner. Even though the possibility of negative volume predictions exists when using Mead's Quadratic Model, it was deemed the superior model of the two investigated.

Étude du développement hétéroblastique chez quelques espèces du genre Medicago

1983 ◽  
Vol 61 (8) ◽  
pp. 2212-2223 ◽  
Author(s):  
Catherine Damerval
Keyword(s):  
Environmental Conditions ◽  
Mean Value ◽  
Annual Species ◽  
The Mean ◽  
Shape Changes

Seven foliar types were defined for the first leaf in the heteroblastic development of seven annual species of Medicago L. Among the species, M. aculeata and M. murex have a typical foliar form. There is no relation between the first leaf and the succeeding trifoliolate one. The shape changes of the middle foliole of the trifoliolate leaves during the development allowed to establish a foliar sequence whose mean length was used to suggest an evolutive hierarchy among the taxa. Five quantitative variables were analysed on the first and on the sixth leaf for stability according to environmental conditions; the two stable variables (L/l and L/Pl) have a best discriminant value for the first leaf than for the sixth one. However, intraspecific heterogeneity is high in both cases. A relation between the flowering precocity and the mean value of one of the sixth leaf's variables (that is, the ratio of the length to the width of the foliole limb) was demonstrated in four species only. The heteroblastic development not only allows to establish a relation between foliar stage and physiologic age, but it itself constitutes a very good taxonomic and systematic criterium; it allowed to identify the seven species studied.

Deep learning to predict subtypes of poorly differentiated lung cancer from biopsy whole slide images.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8536-8536
Author(s):  
Gouji Toyokawa ◽  
Fahdi Kanavati ◽  
Seiya Momosaki ◽  
Kengo Tateishi ◽  
Hiroaki Takeoka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Test Set ◽  
Cancer Subtypes ◽  
Independent Test ◽  
Poorly Differentiated ◽  
Test Sets ◽  
Deep Learning Model ◽  
Whole Slide Images

8536 Background: Lung cancer is the leading cause of cancer-related death in many countries, and its prognosis remains unsatisfactory. Since treatment approaches differ substantially based on the subtype, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC) and small cell lung cancer (SCLC), an accurate histopathological diagnosis is of great importance. However, if the specimen is solely composed of poorly differentiated cancer cells, distinguishing between histological subtypes can be difficult. The present study developed a deep learning model to classify lung cancer subtypes from whole slide images (WSIs) of transbronchial lung biopsy (TBLB) specimens, in particular with the aim of using this model to evaluate a challenging test set of indeterminate cases. Methods: Our deep learning model consisted of two separately trained components: a convolutional neural network tile classifier and a recurrent neural network tile aggregator for the WSI diagnosis. We used a training set consisting of 638 WSIs of TBLB specimens to train a deep learning model to classify lung cancer subtypes (ADC, SCC and SCLC) and non-neoplastic lesions. The training set consisted of 593 WSIs for which the diagnosis had been determined by pathologists based on the visual inspection of Hematoxylin-Eosin (HE) slides and of 45 WSIs of indeterminate cases (64 ADCs and 19 SCCs). We then evaluated the models using five independent test sets. For each test set, we computed the receiver operator curve (ROC) area under the curve (AUC). Results: We applied the model to an indeterminate test set of WSIs obtained from TBLB specimens that pathologists had not been able to conclusively diagnose by examining the HE-stained specimens alone. Overall, the model achieved ROC AUCs of 0.993 (confidence interval [CI] 0.971-1.0) and 0.996 (0.981-1.0) for ADC and SCC, respectively. We further evaluated the model using five independent test sets consisting of both TBLB and surgically resected lung specimens (combined total of 2490 WSIs) and obtained highly promising results with ROC AUCs ranging from 0.94 to 0.99. Conclusions: In this study, we demonstrated that a deep learning model could be trained to predict lung cancer subtypes in indeterminate TBLB specimens. The extremely promising results obtained show that if deployed in clinical practice, a deep learning model that is capable of aiding pathologists in diagnosing indeterminate cases would be extremely beneficial as it would allow a diagnosis to be obtained sooner and reduce costs that would result from further investigations.

The Race for Online Reputation: Implications for Platforms, Firms, and Consumers

2021 ◽  
Author(s):  
Mingwen Yang ◽  
Zhiqiang (Eric) Zheng ◽  
Vijay Mookerjee
Keyword(s):  
Adverse Event ◽  
Marketing Mix ◽  
Digital Economy ◽  
The Other ◽  
The Mean ◽  
The Absolute ◽  
The Cost ◽  
Online Reputation ◽  
Better Than

Online reputation has become a key marketing-mix variable in the digital economy. Our study helps managers decide on the effort they should use to manage online reputation. We consider an online reputation race in which it is important not just to manage the absolute reputation, but also the relative rating. That is, to stay ahead, a firm should try to have ratings that are better than those of its competitors. Our findings are particularly significant for platform owners (such as Expedia or Yelp) to strategically grow their base of participating firms: growing the middle of the market (firms with average ratings) is the best option considering the goals of the platform and the other stakeholders, namely incumbents and consumers. For firms, we find that they should increase their effort when the mean market rating increases. Another key insight for firms is that, sometimes, adversity can come disguised as an opportunity. When an adverse event strikes the industry (such as a reduction in sales margin or an increase in the cost of effort), a firm’s profit can increase if it can manage this event better than its competitors.

Important factors in the tracking of tropical cyclones in operational models

2021 ◽  
Author(s):  
Timothy Marchok
Keyword(s):  
Geophysical Fluid Dynamics Laboratory ◽  
Surface Layers ◽  
Individual Parameter ◽  
Near Surface ◽  
Parameter Configuration ◽  
Multiple Variables ◽  
The Mean ◽  
Tropical Cyclone Center ◽  
Multiple Configurations ◽  
Better Than

AbstractMultiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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