AI Medical Compendium Topic:
Uncertainty

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Quantifying Uncertainty in Deep Learning of Radiologic Images.

Radiology
In recent years, deep learning (DL) has shown impressive performance in radiologic image analysis. However, for a DL model to be useful in a real-world setting, its confidence in a prediction must also be known. Each DL model's output has an estimate...

Uncertainty Estimation with Data Augmentation for Active Learning Tasks on Health Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Supervised machine learning (ML) is revolutionising healthcare, but the acquisition of reliable labels for signals harvested from medical sensors is usually challenging, manual, and costly. Active learning can assist in establishing labels on-the-fly...

Simulating first-order phase transition with hierarchical autoregressive networks.

Physical review. E
We apply the hierarchical autoregressive neural network sampling algorithm to the two-dimensional Q-state Potts model and perform simulations around the phase transition at Q=12. We quantify the performance of the approach in the vicinity of the firs...

Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice.

The Journal of medicine and philosophy
In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level ...

Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC.

Journal of biomechanical engineering
Computational hemodynamic modeling has been widely used in cardiovascular research and healthcare. However, the reliability of model predictions is largely dependent on the uncertainties of modeling parameters and boundary conditions, which should be...

BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.

Protein science : a publication of the Protein Society
Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimating Gibbs free energy change of the protein remained a challenge. Some methods struggle to generalize...

Uncertainty Assessment for Deep Learning Radiotherapy Applications.

Seminars in radiation oncology
In the last 5 years, deep learning applications for radiotherapy have undergone great development. An advantage of radiotherapy over radiological applications is that data in radiotherapy are well structured, standardized, and annotated. Furthermore,...