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Uncertainty

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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...

Uncertainty-aware domain alignment for anatomical structure segmentation.

Medical image analysis
Automatic and accurate segmentation of anatomical structures on medical images is crucial for detecting various potential diseases. However, the segmentation performance of established deep neural networks may degenerate on different modalities or de...

Leveraging spatial uncertainty for online error compensation in EMT.

International journal of computer assisted radiology and surgery
PURPOSE: Electromagnetic tracking (EMT) can potentially complement fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due to the susceptibility to external distortions, systematic error in EMT needs to be compensated algorithmi...

Dynamic performances of a bird-like flapping wing robot under randomly uncertain disturbances.

PloS one
The nonlinear dynamics of a bird-like flapping wing robot under randomly uncertain disturbances was studied in this study. The bird-like flapping wing robot was first simplified into a two-rod model with a spring connection. Then, the dynamic model o...