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Uncertainty

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Preferences in AI algorithms: The need for relevant risk attitudes in automated decisions under uncertainties.

Risk analysis : an official publication of the Society for Risk Analysis
Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a pre...

Adversarially robust neural networks with feature uncertainty learning and label embedding.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks (DNNs) are vulnerable to the attacks of adversarial examples, which bring serious security risks to the learning systems. In this paper, we propose a new defense method to improve the adversarial robustness of DNNs based on stoch...

Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep convolutional neural networks (CNN) are promising for automatic classification of dopamine transporter (DAT)-SPECT images. Reporting the certainty of CNN-based decisions is highly desired to flag cases that might be misclassified and, t...

Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation.

Physics in medicine and biology
Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially pr...

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently int...

Deep learning uncertainty quantification for clinical text classification.

Journal of biomedical informatics
INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the st...

Waste-to-energy incineration site selection framework based on heterogeneous fuzzy regret-PROMETHEE model considering life-cycle carbon emissions.

Environmental science and pollution research international
Waste incineration technology has received extensive attention for its advantages of being harmless, reducing, and recycling. However, the waste-to-energy incineration project confronts significant "not-in-my-backyard (NIMBY) concerns," and irrationa...

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy.

Physics in medicine and biology
Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the u...

Uncertainty-guided cross learning via CNN and transformer for semi-supervised honeycomb lung lesion segmentation.

Physics in medicine and biology
. Deep learning networks such as convolutional neural networks (CNN) and Transformer have shown excellent performance on the task of medical image segmentation, however, the usual problem with medical images is the lack of large-scale, high-quality p...

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information.

Environmental research
Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable poli...