Multi-path x-D recurrent neural networks for collaborative image classification

Gao, Riqiang; Huo, Yuankai; Bao, Shunxing; Tang, Yucheng; Antic, Sanja; Epstein, Emily S.; Deppen, Steve; Paulson, Alexis B.; Sandler, Kim L.; Massion, Pierre P.; Landman, Bennett A.

doi:10.1016/j.neucom.2020.02.033

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…For example, a new method to train predictive models on cohorts of patients using multiple biomarkers, when not all patients have all biomarkers measured, enables training models on larger populations resulting in more generalizable and robust models [30]. Additionally, novel techniques for incorporating longitudinal measurements at irregular intervals and using heterogeneous images allows for more robust treatment of messy, real world imaging data [31][32][33][34].…”

Section: Biomarker Validationmentioning

confidence: 99%

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

Kammer

Deppen

Antic

et al. 2022

CBM

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The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

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Section: Biomarker Validationmentioning

confidence: 99%

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

Kammer

Deppen

Antic

et al. 2022

CBM

Self Cite

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“…Epstein integrates the recursive neural network into the convolutional neural network for image classification. Experiments show that the model is more robust to category-independent attributes [26]. Not only is the wide application range of neural network reflected in its high frequency of use in different fields but also it can be flexibly combined with other networks, models, and methods.…”

Section: Application Of Feedback Neural Networkmentioning

confidence: 99%

“…Let x, y, and z be the time parameters that are measured according to discretization and the discretization step size is 0.01. We consider the length of data which is used to solve numerical equation (26) by the fourth-order Runge-Kutta method is 10000. We select the first component of equation (26) as the data of chaotic background noise x(t).…”

Section: Experimental Simulationsmentioning

confidence: 99%

“…We consider the length of data which is used to solve numerical equation (26) by the fourth-order Runge-Kutta method is 10000. We select the first component of equation (26) as the data of chaotic background noise x(t). With complex autocorrelation and the method of Cao, the embedding dimension can be determined as m � 6 and the delay time can be determined as τ � 7.…”

Section: Experimental Simulationsmentioning

confidence: 99%

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Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Ling

2020

Complexity

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In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

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Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model

Gao

Tang

et al. 2022

Computers in Biology and Medicine

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Multi-path x-D recurrent neural networks for collaborative image classification

Cited by 11 publications

References 19 publications

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model

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