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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: 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 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...

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation.

Yinghuan Shi, Jian Zhang, Tong Ling

IEEE Transactions on Medical Imaging

|October 4, 2021

Summary

This study introduces a new uncertainty estimation method for semi-supervised medical image segmentation. The conservative-radical network (CoraNet) improves segmentation accuracy by identifying uncertain regions.

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Semi-supervised medical image segmentation often relies on entropy for uncertainty estimation.
Existing methods may not fully capture nuanced uncertainty in pixel classifications.

Purpose of the Study:

To propose a novel uncertainty estimation method for semi-supervised medical image segmentation.
To introduce the conservative-radical network (CoraNet) for improved segmentation performance.

Main Methods:

Developed a new uncertainty estimation approach based on pixel misclassification under varying costs.
Proposed the conservative-radical network (CoraNet) with a conservative-radical module (CRM), C-SN, and UC-SN.
Employed an end-to-end training strategy for the CoraNet components.

Main Results:

CoraNet demonstrated superior performance on CT pancreas, MR endocardium, and ACDC multi-structure segmentation tasks.
The method showed significant improvements compared to current state-of-the-art techniques.
Evaluated on diverse public benchmark datasets.

Conclusions:

The proposed uncertainty estimation method effectively identifies uncertain pixels in medical image segmentation.
CoraNet offers a robust and superior approach to semi-supervised medical image segmentation.
The study provides insights into the relationship between the new method and conventional uncertainty estimation techniques.