Uncertainty estimation in AVO inversion using Bayesian dropout based deep learning

2021 ◽  
pp. 109288
Author(s):  
Choi Junhwan ◽  
Oh Seokmin ◽  
Byun Joongmoo
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Avo Inversion

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.

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

2021 ◽  
Vol 6 (2) ◽  
pp. 951-957
Author(s):  
Ze Yang Ding ◽  
Junn Yong Loo ◽  
Vishnu Monn Baskaran ◽  
Surya Girinatha Nurzaman ◽  
Chee Pin Tan
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Soft Robot ◽  
Predictive Uncertainty ◽  
Multimodal Sensing

Uncertainty Estimation for Stereo Matching Based on Evidential Deep Learning

2021 ◽  
pp. 108498
Author(s):  
Chen Wang ◽  
Xiang Wang ◽  
Jiawei Zhang ◽  
Liang Zhang ◽  
Xiao Bai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stereo Matching ◽  
Uncertainty Estimation

scholarly journals A General Framework for Uncertainty Estimation in Deep Learning

2020 ◽  
Vol 5 (2) ◽  
pp. 3153-3160 ◽  
Author(s):  
Antonio Loquercio ◽  
Mattia Segu ◽  
Davide Scaramuzza
Keyword(s):  
Deep Learning ◽  
General Framework ◽  
Uncertainty Estimation

scholarly journals Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction

2020 ◽  
Vol 60 (6) ◽  
pp. 2697-2717 ◽  
Author(s):  
Gabriele Scalia ◽  
Colin A. Grambow ◽  
Barbara Pernici ◽  
Yi-Pei Li ◽  
William H. Green
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Estimation Methods ◽  
Molecular Property ◽  
Property Prediction

scholarly journals Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling

2020 ◽  
Author(s):  
Daniel Klotz ◽  
Frederik Kratzert ◽  
Martin Gauch ◽  
Alden Sampson ◽  
Günter Klambauer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Rainfall Runoff

scholarly journals Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation

2021 ◽  
Author(s):  
Paulo Chagas ◽  
Luiz Souza ◽  
Izabelle Pontes ◽  
Rodrigo Calumby ◽  
Michele Angelo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Nephrotic Syndrome ◽  
Deep Learning ◽  
Membranous Nephropathy ◽  
Uncertainty Estimation ◽  
Glomerular Diseases ◽  
Uncertainty Estimates ◽  
Model Predictions

Membranous Nephropathy (MN) is one of the most common glomerular diseases that cause adult nephrotic syndrome. To assist pathologists on MN classification, we evaluated three deep-learning-based architectures, namely, ResNet-18, DenseNet and Wide-ResNet. In addition, to accomplish more reliable results, we applied Monte-Carlo Dropout for uncertainty estimation. We achieved average F1-Scores above 92% for all models, with Wide-ResNet obtaining the highest average F1-Score (93.2%). For uncertainty estimation on Wide-ResNet, the uncertainty scores showed high relation with incorrect classifications, proving that these uncertainty estimates can support pathologists on the analysis of model predictions.

Deep Learning Based Quantitative Uncertainty Estimation for Ultrasound Shear Wave Elasticity Imaging

2021 ◽  
Author(s):  
Felix Q. Jin ◽  
Lindsey C. Carlson ◽  
Timothy J. Hall ◽  
Helen Feltovich ◽  
Mark L. Palmeri
Keyword(s):  
Deep Learning ◽  
Shear Wave ◽  
Uncertainty Estimation ◽  
Elasticity Imaging ◽  
Shear Wave Elasticity Imaging
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