Personalized Pain Detection in Facial Video with Uncertainty Estimation

2021 ◽  
Author(s):  
Xiaojing Xu ◽  
Virginia R. de Sa
Keyword(s):  
Uncertainty Estimation ◽  
Pain Detection

Development and Certification of Formononetin Reference Material for Quality Control of Functional Foods and Botanical Supplements

2019 ◽  
Vol 15 (5) ◽  
pp. 553-559
Author(s):  
Ningbo Gong ◽  
Baoxi Zhang ◽  
Kun Hu ◽  
Zhaolin Gao ◽  
Guanhua Du ◽  
...  
Keyword(s):  
Quality Control ◽  
Reference Material ◽  
Functional Foods ◽  
Certified Reference Materials ◽  
Coulometric Titration ◽  
Pharmaceutical Formulations ◽  
Uncertainty Estimation ◽  
Expanded Uncertainty ◽  
Scanning Calorimetry ◽  
Botanical Supplements

Background: Formononetin is a common soy isoflavonoid that can be found abundantly in many natural plants. Previous studies have shown that formononetin possesses a variety of activities which can be applied for various medicinal purposes. Certified Reference Materials (CRMs) play a fundamental role in the food, traditional medicine and dietary supplement fields, and can be used for method validation, uncertainty estimation, as well as quality control. Methods: The purity of formononetin was determined by Differential Scanning Calorimetry (DSC), Coulometric Titration (CT) and Mass Balance (MB) methods. Results: This paper reports the sample preparation methodology, homogeneity and stability studies, value assignment, and uncertainty estimation of a new certified reference material of formononetin. DSC, CT and MB methods proved to be sufficiently reliable and accurate for the certification purpose. The purity of the formononetin CRM was therefore found to be 99.40% ± 0.24 % (k = 2) based on the combined value assignments and the expanded uncertainty. Conclusion: This CRM will be a reliable standard for the validation of the analytical methods and for quality assurance/quality control of formononetin and formononetin-related traditional herbs, food products, dietary supplements and pharmaceutical formulations.

scholarly journals Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

2019 ◽  
Vol 5 (1) ◽  
pp. 223-226
Author(s):  
Max-Heinrich Laves ◽  
Sontje Ihler ◽  
Tobias Ortmaier ◽  
Lüder A. Kahrs
Keyword(s):  
Monte Carlo ◽  
Patient Safety ◽  
Deep Learning ◽  
Epistemic Uncertainty ◽  
Computer Aided Diagnosis ◽  
Uncertainty Estimation ◽  
Test Time ◽  
Prediction Uncertainty ◽  
Computer Aided ◽  
Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

PO-1716: Histology correlation of in-vivo [68Ga]PSMAPET/MRI: A method description and uncertainty estimation

2020 ◽  
Vol 152 ◽  
pp. S948
Author(s):  
K. Sandgren ◽  
J. Jonsson ◽  
A. Keeratijarut Lindberg ◽  
T. Näsmark ◽  
S. Said ◽  
...  
Keyword(s):  
Uncertainty Estimation

scholarly journals Calibration and Uncertainty Estimation for Water Content Measurement in Solids

2021 ◽  
Vol 42 (3) ◽  
Author(s):  
Rudolf Aro ◽  
Mohamed Wajdi Ben Ayoub ◽  
Ivo Leito ◽  
Éric Georgin ◽  
Benoit Savanier
Keyword(s):  
Water Vapor ◽  
Water Content ◽  
Software Tool ◽  
Uncertainty Estimation ◽  
Multiple Point ◽  
Content Measurement ◽  
Analysis Technique ◽  
Calibration Methods ◽  
Vapor Analysis ◽  
Secondary Methods

AbstractIn the field of water content measurement, the calibration of coulometric methods (e.g., coulometric Karl Fischer titration or evolved water vapor analysis) is often overlooked. However, as coulometric water content measurement methods are used to calibrate secondary methods, their results must be obtained with the highest degree of confidence. The utility of calibrating such instruments has been recently demonstrated. Both single and multiple point calibration methods have been suggested. This work compares these calibration methods for the evolved water vapor analysis technique. Two uncertainty estimation approaches (Kragten’s spreadsheet and M-CARE software tool) were compared as well, both based on the ISO GUM method.

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