Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Pressure-induced softening of locust bean gum hydrogels: A counterintuitive alternative to freeze-thaw stiffening.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Impact of Concurrent Graft-versus-Host Disease on Long-Term Survival in Critically Ill Patients Following Allogeneic Hematopoietic Stem Cell Transplantation for Hematological Malignancies.

Transplantation and cellular therapy·2026

Same author

Enhancing minority class recovery in high resolution land cover mapping with dynamic imbalance aware oversampling.

Scientific reports·2026

Same author

Haploidentical allogeneic haematopoietic stem cell transplantation for paroxysmal nocturnal haemoglobinuria: a retrospective analysis.

Annals of hematology·2026

Same author

Association of <i>katG</i>, <i>inhA,</i> and <i>AhpC</i> Mutations with Isoniazid Resistance of <i>Mycobacterium tuberculosis</i> in Pulmonary Tuberculosis Patients from Nanjing, China.

Microbial drug resistance (Larchmont, N.Y.)·2026

Same author

Empiric Imipenem/Cilastatin/Relebactam for Febrile Neutropenia After Allogeneic Hematopoietic Stem Cell Transplantation: Two Case Reports.

Journal of blood medicine·2026

Search research articles

Related Experiment Video

Updated: Oct 26, 2025

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

Building robust pathology image analyses with uncertainty quantification.

Jeremias Gomes¹, Jun Kong², Tahsin Kurc³

¹Department of Computer Science, University of Brasília, Brasília, Brazil.

Computer Methods and Programs in Biomedicine

|August 1, 2021

Summary

This study quantifies uncertainty in digital pathology image analysis using uncertainty quantification (UQ) and sensitivity analysis (SA). Feature selection strategies improved patient grouping stability in survival analysis, enhancing confidence in digital pathology results.

Keywords:

Microscopy Sensitivity analysis Survival analysis Uncertainty quantification Whole slide image analysis

More Related Videos

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Published on: August 3, 2018

Related Experiment Videos

Last Updated: Oct 26, 2025

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Published on: August 3, 2018

Area of Science:

Digital pathology
Computational pathology
Biomedical image analysis

Background:

Computerized pathology image analysis is crucial for quantitative tissue characterization and aiding pathologists.
Uncertainty in these analyses can impact research and clinical applications.

Purpose of the Study:

To systematically quantify and minimize uncertainty in computer-based pathology image analysis outputs.
To develop methods for selecting stable features that reduce application output uncertainty.

Main Methods:

Employed uncertainty quantification (UQ) and sensitivity analysis (SA) methods (Variance-Based Decomposition, Morris One-At-a-Time) on large Whole Slide Imaging datasets (BRCA, LUSC).
Combined high-performance computing with efficient UQ/SA methods for compute-intensive studies.
Identified parameters and nuclear features impacting results and uncertainty.

Main Results:

Input parameter variations significantly affect segmentation, feature computation, and survival analysis stages.
Features were classified by robustness to parameter variation.
Feature selection strategy improved patient grouping stability in survival analysis by 17% (BRCA) and 34% (LUSC).

Conclusions:

The developed strategy enhances analysis robustness.
Sensitivity analysis (SA) and uncertainty quantification (UQ) are vital for increasing confidence in digital pathology.