A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
Reliable neural networks for regression uncertainty estimation
While deep neural networks are highly performant and successful in a wide range of real-
world problems, estimating their predictive uncertainty remains a challenging task. To …
world problems, estimating their predictive uncertainty remains a challenging task. To …
Reduced order probabilistic emulation for physics‐based thermosphere models
The geospace environment is volatile and highly driven. Space weather has effects on
Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere …
Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere …
Flight dynamic uncertainty quantification modeling using physics-informed neural networks
When attempting to develop aerodynamic models for extreme flight conditions, including
high angle of attack, high rotational rates, and tumbling motion, many classical methods …
high angle of attack, high rotational rates, and tumbling motion, many classical methods …
Nonlinear regression of remaining surgery duration from videos via Bayesian LSTM-based deep negative correlation learning
In this paper, we address the problem of estimating remaining surgery duration (RSD) from
surgical video frames. We propose a Bayesian long short-term memory (LSTM) network …
surgical video frames. We propose a Bayesian long short-term memory (LSTM) network …
Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
MRI reconstruction techniques based on deep learning have led to unprecedented
reconstruction quality especially in highly accelerated settings. However, deep learning …
reconstruction quality especially in highly accelerated settings. However, deep learning …
Towards reliable uncertainty quantification via deep ensemble in multi-output regression task
S Yang, K Yee - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
This study aims to comprehensively investigate the deep ensemble approach, an
approximate Bayesian inference, in the multi-output regression task for predicting the …
approximate Bayesian inference, in the multi-output regression task for predicting the …
Uncertainty analysis of Altantic salmon fish scale's acoustic impedance using 30 MHz C-Scan measurements
Understanding the biomechanics of fish scales is crucial for their survival and adaptation.
Ultrasonic Cscan measurements offer a promising tool for non-invasive characterization …
Ultrasonic Cscan measurements offer a promising tool for non-invasive characterization …
[HTML][HTML] Stochastic modeling of physical drag coefficient–Its impact on orbit prediction and space traffic management
Ambitious satellite constellation projects by commercial entities and the ease of access to
space in recent times have led to a dramatic proliferation of low-Earth space traffic. It …
space in recent times have led to a dramatic proliferation of low-Earth space traffic. It …
Unsupervised knowledge-transfer for learned image reconstruction
R Barbano, Ž Kereta, A Hauptmann… - Inverse …, 2022 - iopscience.iop.org
Deep learning-based image reconstruction approaches have demonstrated impressive
empirical performance in many imaging modalities. These approaches usually require a …
empirical performance in many imaging modalities. These approaches usually require a …