Literature DB >> 28519985

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

B Sahiner1.   

Abstract

Standalone performance evaluation of a CAD system provides information about the abnormality detection or classification performance of the computerized system alone. Although the performance of the reader with CAD is the final step in CAD system assessment, standalone performance evaluation is an important component for several reasons: First, standalone evaluation informs the reader about the performance level of the CAD system and may have an impact on how the reader uses the system. Second, it provides essential information to the system designer for algorithm optimization during system development. Third, standalone evaluation can provide a detailed description of algorithm performance (e.g., on subgroups of the population) because a larger data set with more samples from different subgroups can be included in standalone studies compared to reader studies. Proper standalone evaluation of a CAD system involves a number of key components, some of which are shared with the assessment of reader performance with CAD. These include (1) selection of a test data set that allows performance assessment with little or no bias and acceptable uncertainty; (2) a reference standard that indicates disease status as well as the location and extent of disease; (3) a clearly defined method for labeling each CAD mark as a true-positive or false-positive; and (4) a properly selected set of metrics to summarize the accuracy of the computer marks and their corresponding scores. In this lecture, we will discuss various approaches for the key components of standalone CAD performance evaluation listed above, and present some of the recommendations and opinions from the AAPM CAD subcommittee on these issues. Learning Objectives 1. Identify basic components and metrics in the assessment of standalone CAD systems 2. Understand how each component may affect the assessed performance 3. Learn about AAPM CAD subcommittee's opinions and recommendations on factors and metrics related to the evaluation of standalone CAD system performance.
© 2012 American Association of Physicists in Medicine.

Keywords:  Computer hardware; Computer simulation; Lectures

Year:  2012        PMID: 28519985     DOI: 10.1118/1.4736169

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  1 in total

1.  Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use.

Authors:  Zhimin Huo; Ronald M Summers; Sophie Paquerault; Joseph Lo; Jeffrey Hoffmeister; Samuel G Armato; Matthew T Freedman; Jesse Lin; Shih-Chung Ben Lo; Nicholas Petrick; Berkman Sahiner; David Fryd; Hiroyuki Yoshida; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-07       Impact factor: 4.071

  1 in total

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