[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …
the present era of big data. Machine learning and probability theory methods have been …
Neural network-based processing and reconstruction of compromised biophotonic image data
In recent years, the integration of deep learning techniques with biophotonic setups has
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …
Convergence of Stein variational gradient descent under a weaker smoothness condition
L Sun, A Karagulyan… - … Conference on Artificial …, 2023 - proceedings.mlr.press
Abstract Stein Variational Gradient Descent (SVGD) is an important alternative to the
Langevin-type algorithms for sampling from probability distributions of the form $\pi …
Langevin-type algorithms for sampling from probability distributions of the form $\pi …
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 …
Efficient Bayesian computation for low-photon imaging problems
This paper studies a new and highly efficient Markov chain Monte Carlo (MCMC)
methodology to perform Bayesian inference in low-photon imaging problems, with particular …
methodology to perform Bayesian inference in low-photon imaging problems, with particular …
Robustness via uncertainty-aware cycle consistency
Unpaired image-to-image translation refers to learning inter-image-domain mapping without
corresponding image pairs. Existing methods learn deterministic mappings without explicitly …
corresponding image pairs. Existing methods learn deterministic mappings without explicitly …
Conditional variational autoencoder for learned image reconstruction
Learned image reconstruction techniques using deep neural networks have recently gained
popularity and have delivered promising empirical results. However, most approaches focus …
popularity and have delivered promising empirical results. However, most approaches focus …
Radiation image reconstruction and uncertainty quantification using a Gaussian process prior
J Lee, TH Joshi, MS Bandstra, DL Gunter, BJ Quiter… - Scientific Reports, 2024 - nature.com
We propose a complete framework for Bayesian image reconstruction and uncertainty
quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum …
quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum …
Proximal nested sampling for high-dimensional Bayesian model selection
Bayesian model selection provides a powerful framework for objectively comparing models
directly from observed data, without reference to ground truth data. However, Bayesian …
directly from observed data, without reference to ground truth data. However, Bayesian …