AIMC Topic: Uncertainty

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UNIQUE: A Framework for Uncertainty Quantification Benchmarking.

Journal of chemical information and modeling Nov 14, 2024
Machine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or exp...
Uncertainty Machine Learning Algorithms Drug Discovery
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A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.

Medical image analysis Nov 10, 2024
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image reg...
Uncertainty Deep Learning Humans Image Processing, Computer-Assisted
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Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Molecular pharmaceutics Nov 7, 2024
Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive ...
Uncertainty Pharmacokinetics Neural Networks, Computer Animals Bayes Theorem Drug Discovery Humans Models, Biological Drug Development
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Mitigating Diagnostic Errors in Lung Cancer Classification: A Multi-Eyes Principle to Uncertainty Quantification.

IEEE journal of biomedical and health informatics Nov 6, 2024
In radiology, particularly in lung cancer diagnosis, diagnostic errors and cognitive biases pose substantial challenges. These issues, including perceptual errors, interpretive mistakes, and cognitive biases such as anchoring and premature closure, a...
Uncertainty Lung Neoplasms Monte Carlo Method Humans Diagnostic Errors Deep Learning
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Uncertainty-Aware Health Diagnostics via Class-Balanced Evidential Deep Learning.

IEEE journal of biomedical and health informatics Nov 6, 2024
Uncertainty quantification is critical for ensuring the safety of deep learning-enabled health diagnostics, as it helps the model account for unknown factors and reduces the risk of misdiagnosis. However, existing uncertainty quantification studies o...
Humans Deep Learning Uncertainty Algorithms Diagnosis, Computer-Assisted
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The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

JMIR medical education Nov 4, 2024
In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient's problem, performing the physical examination, interpreting the finding...
Artificial Intelligence Physician-Patient Relations Education, Medical Uncertainty Humans
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Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.

Science advances Oct 30, 2024
Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed b...
Adult Cognition Humans Uncertainty Decision Making Neural Networks, Computer Male
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Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.

The Journal of arthroplasty Oct 29, 2024
BACKGROUND: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardwar...
Radiography Humans Registries Knee Prosthesis Knee Joint Retrospective Studies Uncertainty Arthroplasty, Replacement, Knee Deep Learning
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Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water research Oct 25, 2024
Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, the...
Chlorine Water Quality Uncertainty Temperature Models, Theoretical Unsupervised Machine Learning
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Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

Journal of chemical information and modeling Oct 23, 2024
The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties,...
Uncertainty Monte Carlo Method Thermodynamics Kinetics Deep Learning
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