AIMC Topic: Uncertainty

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Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set.

Magnetic resonance in medicine Jan 28, 2024
PURPOSE: To estimate pixel-wise predictive uncertainty for deep learning-based MR image reconstruction and to examine the impact of domain shifts and architecture robustness.
Magnetic Resonance Imaging Uncertainty Deep Learning Image Processing, Computer-Assisted Brain Algorithms Humans Databases, Factual
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Evidence-based uncertainty-aware semi-supervised medical image segmentation.

Computers in biology and medicine Jan 24, 2024
Semi-Supervised Learning (SSL) has demonstrated great potential to reduce the dependence on a large set of annotated data, which is challenging to collect in clinical practice. One of the most important SSL methods is to generate pseudo labels from t...
Image Processing, Computer-Assisted Benchmarking Supervised Machine Learning Uncertainty
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Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

Physics in medicine and biology Jan 24, 2024
. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consumi...
Deep Learning Image Processing, Computer-Assisted Humans Reproducibility of Results Radiotherapy Planning, Computer-Assisted Uncertainty Lung Neoplasms Organs at Risk
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Prediction on nature of cancer by fuzzy graphoidal covering number using artificial neural network.

Artificial intelligence in medicine Jan 18, 2024
Predicting the chances of various types of cancers for different organs in the human body is a typical decision-making process in medicine and health. The signaling pathways have played a vital role in increasing or decreasing the possibility of the ...
Neoplasms Humans Uncertainty Fuzzy Logic Neural Networks, Computer Algorithms
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AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence.

Journal of chemical information and modeling Jan 17, 2024
The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural ne...
Neural Networks, Computer Artificial Intelligence Uncertainty
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Dual consistency regularization with subjective logic for semi-supervised medical image segmentation.

Computers in biology and medicine Jan 15, 2024
Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical image analysis. It significantly reduces the time and cost involved in labeling data. Current methods primarily focus on consistency regularization and the ...
Uncertainty Image Processing, Computer-Assisted Supervised Machine Learning
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The probable future of toxicology - probabilistic risk assessment.

ALTEX Jan 12, 2024
Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasing...
Risk Assessment Toxicology Animal Testing Alternatives Uncertainty Artificial Intelligence Animals Humans
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State identification for a class of uncertain switched systems by differential neural networks.

Network (Bristol, England) Jan 11, 2024
This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guarante...
Algorithms Computer Simulation Humans Neural Networks, Computer Uncertainty Gait Nonlinear Dynamics
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A QUEST for Model Assessment: Identifying Difficult Subgroups via Epistemic Uncertainty Quantification.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increase...
Benchmarking Probability Neural Networks, Computer Uncertainty Humans Machine Learning
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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 Jan 6, 2024
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...
Humans Risk Assessment Decision Making Attitude Software Artificial Intelligence Uncertainty Algorithms
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