Results 31 to 40 of about 10,063 (188)

Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference

Sensors, 2022
In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years.
Monika E. Heringhaus, Yi Zhang, André Zimmermann, Lars Mikelsons   +3 more
doaj   +1 more source

Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging

Machine Learning for Biomedical Imaging, 2021
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated.
Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, Tobias Ortmaier   +4 more
openaire   +2 more sources

Cold Posteriors and Aleatoric Uncertainty

, 2020
5 pages, 3 ...
Adlam, Ben, Snoek, Jasper, Smith, Samuel L.   +2 more
openaire   +2 more sources

Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

, 2017
Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly
Bhattacharyya, Apratim, Fritz, Mario, Schiele, Bernt   +2 more
core   +1 more source

Decoding Uncertainty Quantification for Oncology—An Illustration Using Radiomics

Diagnostics
While AI models are developed in oncology for predicting different clinical outcomes, the focus is often on accuracy and many fail to adequately communicate the degree of certainty in these predictions.
Florian van Daalen, Balu Krishna Sasidharan, C. Praveenraj, Amal Joseph Varghese, Andre Dekker, Leonard Wee, Rianne Fijten, Aparna Irodi, Hannah Mary T. Thomas   +8 more
doaj   +1 more source

Uncertainty Quantification for Full-Flight Data Based Engine Fault Detection with Neural Networks

Machines, 2022
Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault ...
Matthias Weiss, Stephan Staudacher, Jürgen Mathes, Duilio Becchio, Christian Keller   +4 more
doaj   +1 more source

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022
Accepted in IEEE International Symposium on Biomedical Imaging (ISBI ...
Sambyal, Abhishek Singh, Krishnan, Narayanan C., Bathula, Deepti R.   +2 more
openaire   +2 more sources

Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations

Entropy
Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification.
Timothy Davey
doaj   +1 more source

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

, 2018
Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning.
A Carass, A Lang, A Mishra, J Novosel, JE Iglesias, JH Acton, KA Vermeer, L Fang, MK Garvin, O Ronneberger, Pratul P. Srinivasan, Sina Farsiu, SJ Chiu   +12 more
core   +1 more source

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of object boundaries. Estimating this inherent uncertainty and predicting multiple plausible hypotheses is of great interest in many applications, yet this ability is lacking in ...
Monteiro, M, Folgoc, LL, Castro, DCD, Pawlowski, N, Marques, B, Kamnitsas, K, Wilk, MVD, Glocker, B   +7 more
openaire   +3 more sources