scholarly journals Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles

2022 ◽  
Vol 268 ◽  
pp. 112760
Author(s):  
Nico Lang ◽  
Nikolai Kalischek ◽  
John Armston ◽  
Konrad Schindler ◽  
Ralph Dubayah ◽  
...  
Keyword(s):  
Uncertainty Estimation ◽  
Canopy Height

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 Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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
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