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Evaluation of the bootstrap smoothed conditional (BSC) interval and others in small sample FROC analysis of mammography CAD system performance
Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.
Article in journal (Refereed) Submitted
URN: urn:nbn:se:uu:diva-95400OAI: oai:DiVA.org:uu-95400DiVA: diva2:169590
Available from: 2007-01-26 Created: 2007-01-26Bibliographically approved
In thesis
1. Resampling Evaluation of Signal Detection and Classification: With Special Reference to Breast Cancer, Computer-Aided Detection and the Free-Response Approach
Open this publication in new window or tab >>Resampling Evaluation of Signal Detection and Classification: With Special Reference to Breast Cancer, Computer-Aided Detection and the Free-Response Approach
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The first part of this thesis is concerned with trend modelling of breast cancer mortality rates. By using an age-period-cohort model, the relative contributions of period and cohort effects are evaluated once the unquestionable existence of the age effect is controlled for. The result of such a modelling gives indications in the search for explanatory factors. While this type of modelling is usually performed with 5-year period intervals, the use of 1-year period data, as in Paper I, may be more appropriate.

The main theme of the thesis is the evaluation of the ability to detect signals in x-ray images of breasts. Early detection is the most important tool to achieve a reduction in breast cancer mortality rates, and computer-aided detection systems can be an aid for the radiologist in the diagnosing process.

The evaluation of computer-aided detection systems includes the estimation of distributions. One way of obtaining estimates of distributions when no assumptions are at hand is kernel density estimation, or the adaptive version thereof that smoothes to a greater extent in the tails of the distribution, thereby reducing spurious effects caused by outliers. The technique is described in the context of econometrics in Paper II and then applied together with the bootstrap in the breast cancer research area in Papers III-V.

Here, estimates of the sampling distributions of different parameters are used in a new model for free-response receiver operating characteristic (FROC) curve analysis. Compared to earlier work in the field, this model benefits from the advantage of not assuming independence of detections in the images, and in particular, from the incorporation of the sampling distribution of the system's operating point.

Confidence intervals obtained from the proposed model with different approaches with respect to the estimation of the distributions and the confidence interval extraction methods are compared in terms of coverage and length of the intervals by simulations of lifelike data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2007. 33 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 23
Statistics, breast cancer, trend modelling, FROC, confidence intervals, threshold independence, bootstrap, kernel density estimation, mammography, computer-aided detection, Statistik
urn:nbn:se:uu:diva-7452 (URN)978-91-554-6783-8 (ISBN)
Public defence
2007-02-16, Hörsal 2, Ekonomikum, Kyrkogårdsgatan 10, Uppsala, 13:15
Available from: 2007-01-26 Created: 2007-01-26 Last updated: 2014-08-18Bibliographically approved

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