AIMC Topic: Uncertainty

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Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In t...

Quantifying uncertainty in machine learning classifiers for medical imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical...

Challenges and Opportunities for Bayesian Statistics in Proteomics.

Journal of proteome research
Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of ...

Forecasting the realized variance of oil-price returns: a disaggregated analysis of the role of uncertainty and geopolitical risk.

Environmental science and pollution research international
We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical ...

Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation.

IEEE journal of biomedical and health informatics
Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1 The code will be available from https://github.com/JohnleeHIT/SLE...

Uncertainty-aware hierarchical segment-channel attention mechanism for reliable and interpretable multichannel signal classification.

Neural networks : the official journal of the International Neural Network Society
Multichannel signal data analysis has been crucial in various industrial applications, such as human activity recognition, vehicle failure predictions, and manufacturing equipment monitoring. Recently, deep neural networks have come into use for mult...

Uncertainty-aware skin cancer detection: The element of doubt.

Computers in biology and medicine
Artificial intelligence (AI)-based medical diagnosis has received huge attention due to its potential to improve and accelerate the decision-making process at the patient level in a range of healthcare settings. Despite the recent signs of progress i...

Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation.

International journal of neural systems
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by regularizing the learning of limited well-annotated data with the knowledge provided by a large amount of unlabeled data. However, there are many misuses and u...

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting.

Computational intelligence and neuroscience
In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour...

Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques.

Computational intelligence and neuroscience
This study presents an optimization approach for scheduling the operation room for emergency surgeries, considering the priority of surgeries. This optimization model aims to minimize the costs associated with elective and emergency surgeries and max...