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Uncertainty

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Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty.

Medical image analysis
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measure...

Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions.

Environmental science and pollution research international
Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events...

Sustainability prioritization of sewage sludge to energy scenarios with hybrid-data consideration: a fuzzy decision-making framework based on full consistency method and fusion ranking model.

Environmental science and pollution research international
This work proposed a novel mathematical framework for the sustainability assessment of sewage sludge to energy (SStE) scenarios, by resorting to fuzzy multi-criteria decision-making (MCMD) methods. In which, an evaluation system including twelve crit...

Boundary Mittag-Leffler stabilization of fractional reaction-diffusion cellular neural networks.

Neural networks : the official journal of the International Neural Network Society
Mittag-Leffler stabilization is studied for fractional reaction-diffusion cellular neural networks (FRDCNNs) in this paper. Different from previous literature, the FRDCNNs in this paper are high-dimensional systems, and boundary control and observed-...

A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy.

Physics in medicine and biology
We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information o...

Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty.

NeuroImage
MRI-based brain age prediction has been widely used to characterize normal brain development, and deviations from the typical developmental trajectory are indications of brain abnormalities. Age prediction of the fetal brain remains unexplored, altho...

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks.

Physics in medicine and biology
Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation positio...

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...