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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

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Uncertainty Estimation in Medical Image Classification: Systematic Review.

Alexander Kurz¹, Katja Hauser¹, Hendrik Alexander Mehrtens¹

¹Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.

JMIR Medical Informatics

|August 2, 2022

Summary

Deep neural networks require uncertainty estimation for reliable medical image classification. This review analyzes methods and metrics for uncertainty estimation in AI-driven medical image analysis.

Keywords:

deep learning medical image classification medical imaging network calibration out-of-distribution detection uncertainty estimation

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Science

Background:

Deep neural networks (DNNs) achieve high accuracy in medical image classification.
Estimating prediction uncertainty is crucial for real-world medical AI applications.
Uncertainty estimation enhances the reliability and trustworthiness of AI diagnostic tools.

Purpose of the Study:

To review uncertainty estimation techniques applied to medical image classification.
To identify and evaluate metrics used for assessing uncertainty estimation effectiveness.
To provide a comprehensive overview of the current landscape of uncertainty in medical AI.

Main Methods:

Systematic literature review of peer-reviewed studies (2016-2021).
Searched major academic databases (Google Scholar, PubMed, IEEE Xplore, ScienceDirect).
Keywords included "uncertainty estimation," "medical image classification," and related terms.

Main Results:

22 papers were selected for detailed analysis.
A systematic comparison table of uncertainty estimation methods is presented.
Sampling-based methods like Monte-Carlo Dropout and Deep Ensembles are frequently utilized.

Conclusions:

Uncertainty estimation methods in medical imaging are diverse.
Monte-Carlo Dropout and Deep Ensembles are the most common approaches.
Future research should explore uncertainty estimation in human-AI collaborative diagnostic systems.