Journal Article10.1118/1.598341
A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms.
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TL;DR: An automated method is developed for the determination of the parameter values that maximize the performance of a mammographic CAD scheme that utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures theperformance of the scheme.
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Abstract: Computer-aided diagnosis(CAD) schemes have the potential of substantially increasing diagnostic accuracy in mammography by providing the advantages of having a second reader. Our laboratory has developed a CAD scheme for detecting clustered microcalcifications in digital mammograms that is being tested clinically at the University of Chicago Hospitals. Our CAD scheme contains a large number of parameters such as filter weights, threshold levels, and region of interest (ROI) sizes. The choice of these parameter values determines the overall performance of the system and thus must be carefully set. Unfortunately, when the number of parameters becomes large, it is very difficult to obtain the optimal performance, especially when the values of the parameters are correlated with each other. In this study, we address the problem of identifying the optimal overall performance by developing an automated method for the determination of the parameter values that maximize the performance of a mammographicCAD scheme. Our method utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures the performance of the scheme. Using a database of 89 digitized mammograms, our method demonstrated that the sensitivity of our CAD scheme can be increased from 80% to 87% at a false positive rate of 1.0 per image. We estimate the average performance of our CAD scheme on unknown cases by performing jackknife tests; this was previously not feasible when the parameters of the CAD scheme were determined in a nonautomated manner.
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Citations
Computer-aided detection and classification of microcalcifications in mammograms: a survey
TL;DR: The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control.
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Full breast digital mammography with an amorphous silicon‐based flat panel detector: Physical characteristics of a clinical prototype
Srinivasan Vedantham,Andrew Karellas,Sankararaman Suryanarayanan,Douglas Albagli,Sung Han,Eric J. Tkaczyk,Cynthia Elizabeth Landberg,Beale Opsahl-Ong,Paul R. Granfors,Ilias Levis,Carl J. D'Orsi,R. Edward Hendrick +11 more
TL;DR: The response of the imager was linear and did not exhibit signal saturation under tested exposure conditions, and Detector element nonuniformity and electronic gain variations were not significant after appropriate calibration and software corrections.
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Current status and future directions of computer-aided diagnosis in mammography
TL;DR: CADe has been shown to help radiologists find more cancers both in observer studies and in clinical evaluations, and Clinically, CADe increases the number of cancers detected by approximately 10%, which is comparable to double reading by two radiologists.
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Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t -SNE
TL;DR: In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities and were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation.
165
Computer-aided diagnosis of breast lesions in medical images
TL;DR: Given current error rates, this article surveys various approaches and techniques for improved breast lesion diagnosis in medical images, including mammography, ultrasound and magnetic resonance imaging.
158
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Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography
TL;DR: The potential application of such a computer-aided system to mammographic interpretation is demonstrated by its ability to detect microcalcifications in clinical mammograms.
407
Sensitivity and specificity of first screen mammography in the Canadian National Breast Screening Study: a preliminary report from five centers.
Cornelia J. Baines,Anthony B. Miller,C Wall,D V McFarlane,I S Simor,R Jong,B J Shapiro,L Audet,M Petitclerc,D Ouimet-Oliva +9 more
TL;DR: Sensitivity and specificity of first screen mammography in a randomized screening trial at five centers are reported and all 206 cancer cases were histologically confirmed, and 174 were defined as being detectable at first screening.
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An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms
TL;DR: The wavelet transform approach can improve the detection of subtle clustered microcalcifications in mammograms and was useful in detecting some of the subtle microCalcifications that were not detected by the previous scheme, which was based on the difference-image technique.
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