AIMC Topic: Uncertainty

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Uncertainty-aware deep learning for segmentation of primary tumor and pathologic lymph nodes in oropharyngeal cancer: Insights from a multi-center cohort.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PURPOSE: Information on deep learning (DL) tumor segmentation accuracy on a voxel and a structure level is essential for clinical introduction. In a previous study, a DL model was developed for oropharyngeal cancer (OPC) primary tumor (PT) segmentati...

A decision-making framework for evaluating medical equipment suppliers under uncertainty.

Scientific reports
The procurement of medical equipment is a critical concern for healthcare organizations striving to deliver comprehensive patient care. Thus, the procurement process, including performance evaluation and selection of medical equipment suppliers, pose...

Performance of Plug-In Augmented ChatGPT and Its Ability to Quantify Uncertainty: Simulation Study on the German Medical Board Examination.

JMIR medical education
BACKGROUND: The GPT-4 is a large language model (LLM) trained and fine-tuned on an extensive dataset. After the public release of its predecessor in November 2022, the use of LLMs has seen a significant spike in interest, and a multitude of potential...

Estimating uncertainty from feed-forward network based sensing using quasi-linear approximation.

Neural networks : the official journal of the International Neural Network Society
A fundamental problem in neural network theory is the quantification of uncertainty as it propagates through these constructs. Such quantification is crucial as neural networks become integrated into broader engineered systems that render decisions b...

A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty.

Environmental monitoring and assessment
Accurate simulation of groundwater level is crucial for the sustainable management of water resources. However, the numerous uncertainties in input data, simulation model parameters, and physical processes, as well as the dependency between system va...

UC-NeRF: Uncertainty-Aware Conditional Neural Radiance Fields From Endoscopic Sparse Views.

IEEE transactions on medical imaging
Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning,...

A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.

Medical image analysis
Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as va...

Control of medical digital twins with artificial neural networks.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
The objective of precision medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dyn...

Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo study.

Computers in biology and medicine
The increasing use of ionizing radiation has raised concerns about adverse and long-term health risks for individuals. Therefore, to evaluate the range of risks and protection against ionizing radiation, it is necessary to assess the dosimetry calcul...

CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.

Nature communications
Estimation of enzymatic activities still heavily relies on experimental assays, which can be cost and time-intensive. We present CatPred, a deep learning framework for predicting in vitro enzyme kinetic parameters, including turnover numbers (k), Mic...