AIMC Topic: Uncertainty

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Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...

Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing.

Journal of digital imaging
The ideal radiology report reduces diagnostic uncertainty, while avoiding ambiguity whenever possible. The purpose of this study was to characterize the use of uncertainty terms in radiology reports at a single institution and compare the use of thes...

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction.

Journal of chemical information and modeling
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and r...

Left ventricle quantification with sample-level confidence estimation via Bayesian neural network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks w...

Integrating uncertainty in deep neural networks for MRI based stroke analysis.

Medical image analysis
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicin...

Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities.

Neural networks : the official journal of the International Neural Network Society
In this paper, the protocol-based remote state estimation problem is considered for a kind of delayed artificial neural networks. The random time-varying delays fall into certain intervals with known probability distributions. For the sake of reducin...

Controller design for finite-time and fixed-time stabilization of fractional-order memristive complex-valued BAM neural networks with uncertain parameters and time-varying delays.

Neural networks : the official journal of the International Neural Network Society
In this paper we investigate controller design problem for finite-time and fixed-time stabilization of fractional-order memristive complex-valued BAM neural networks (FMCVBAMNNs) with uncertain parameters and time-varying delays. By using the Lyapuno...

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Environmental science and pollution research international
Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input sce...

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation.

Medical image analysis
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely expensive in the field of medical image analysis. Unlabeled data, on the...

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

Scientific reports
During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alt...