A fast ?Monte-Carlo cross-validation? procedure for large least squares problems with noisy data

1989 ◽  
Vol 56 (1) ◽  
pp. 1-23 ◽  
Author(s):  
A. Girard
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Cross Validation ◽  
Noisy Data ◽  
Least Squares Problems ◽  
Validation Procedure ◽  
Monte Carlo Cross Validation

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

Monte Carlo cross validation

2001 ◽  
Vol 56 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Qing-Song Xu ◽  
Yi-Zeng Liang
Keyword(s):  
Monte Carlo ◽  
Cross Validation ◽  
Monte Carlo Cross Validation

scholarly journals Revealing pathway cross-talk related to diabetes mellitus by Monte Carlo Cross-Validation analysis

2017 ◽  
Vol 12 (1) ◽  
pp. 473-480
Author(s):  
Han-Qing Cai ◽  
Shi-Hong Lv ◽  
Chun-Jing Shi
Keyword(s):  
Diabetes Mellitus ◽  
Monte Carlo ◽  
Cross Talk ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Pathway Crosstalk ◽  
High Area ◽  
Pathways Analysis ◽  
Auc Value ◽  
Monte Carlo Cross Validation

AbstractObjectiveTo explore potential functional biomarkers in diabetes mellitus (DM) by utilizing gene pathway cross-talk.MethodsFirstly, potential disrupted pathways that were enriched by differentially expressed genes (DEGs) were identified based on biological pathways downloaded from the Ingenuity Pathways Analysis (IPA) database. In addition, we quantified the pathway crosstalk for each pair of pathways based on Discriminating Score (DS). Random forest (RF) classification was then employed to find the top 10 pairs of pathways with a high area under the curve (AUC) value between DM samples versus normal samples based on 10-fold cross-validation. Finally, a Monte Carlo Cross-Validation was applied to demonstrate the identified pairs of pathways by a mutual information analysis.ResultsA total of 247 DEGs in normal and disease samples were identified. Based on the F-test, 50 disrupted pathways were obtained with false discovery rate (FDR) < 0.01. Simultaneously, after calculating the DS, the top 10 pairs of pathways were selected based on a higher AUC value as measured by RF classification. From the Monte Carlo Cross-Validation, we considered the top 10 pairs of pathways with higher AUC values ranked for all 50 bootstraps as the most frequently detected ones.ConclusionThe pairs of pathways identified in our study might be key regulators in DM.

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