Jove
Visualize
Contact Us

Related Concept Videos

Statgraphics01:10

Statgraphics

446
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
446
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

375
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
375
Feedback control systems01:26

Feedback control systems

746
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
746

You might also read

Related Articles

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

Sort by
Same author

FOXP3-stained image analysis for follicular lymphoma: Optimal adaptive thresholding with maximal nucleus coverage.

Proceedings of SPIE--the International Society for Optical Engineering·2017
Same author

Treatment response assessment of head and neck cancers on CT using computerized volume analysis.

AJNR. American journal of neuroradiology·2010
Same author

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

IEEE transactions on medical imaging·2002
Same author

Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.

Medical physics·2002
Same author

Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques.

Medical physics·2001
Same author

Improvement of mammographic mass characterization using spiculation meausures and morphological features.

Medical physics·2001
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 2, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

999

WE-E-217A-02: Methodologies for Evaluation of Standalone CAD System Performance.

B Sahiner1

  • 1US Food and Drug Administration, Silver Spring, MD.

Medical Physics
|May 19, 2017
PubMed
Summary
This summary is machine-generated.

Standalone performance evaluation of computerized detection (CAD) systems is crucial for assessing algorithm accuracy and informing reader use. This evaluation guides system design and provides detailed performance insights across diverse patient groups.

Keywords:
Computer hardwareComputer simulationLectures

More Related Videos

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

500
Evaluation of Respiratory System Mechanics in Mice using the Forced Oscillation Technique
13:10

Evaluation of Respiratory System Mechanics in Mice using the Forced Oscillation Technique

Published on: May 15, 2013

58.4K

Related Experiment Videos

Last Updated: Mar 2, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

999
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

500
Evaluation of Respiratory System Mechanics in Mice using the Forced Oscillation Technique
13:10

Evaluation of Respiratory System Mechanics in Mice using the Forced Oscillation Technique

Published on: May 15, 2013

58.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Standalone performance evaluation of Computer-Aided Detection (CAD) systems is essential, distinct from reader performance assessments.
  • It informs clinicians on CAD system capabilities and aids developers in algorithm optimization.
  • Standalone studies allow for larger datasets and subgroup analysis, offering deeper insights into CAD performance.

Purpose of the Study:

  • To discuss key components of standalone CAD system performance evaluation.
  • To present recommendations and opinions from the AAPM CAD subcommittee.
  • To enhance understanding of CAD system accuracy and its clinical application.

Main Methods:

  • Selection of unbiased test datasets with acceptable uncertainty.
  • Establishment of a reference standard for disease status, location, and extent.
  • Definition of methods for classifying CAD marks (true-positive/false-positive).
  • Selection of appropriate metrics for summarizing accuracy and scores.

Main Results:

  • Identified critical components for robust standalone CAD evaluation.
  • Highlighted the importance of data set selection, reference standards, and clear labeling methods.
  • Emphasized the role of suitable metrics in summarizing CAD performance.

Conclusions:

  • Proper standalone evaluation is vital for understanding CAD system performance.
  • Key components include data selection, reference standards, mark classification, and performance metrics.
  • The AAPM CAD subcommittee provides recommendations for optimizing this evaluation process.