R. M. Nishikawa, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, "Computer-aided detection of clustered microcalcifications on digital mammograms," Med. Biol. Eng. Comput., vol. 33, pp. 174--178, 1995. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Multiobjective Genetic Optimization of Diagnostic.. - Kupinski, Anastasio (1999) (14 citations) (Correct)
....of Radiology, The University of Chicago, Chicago, IL 60637 USA. M. A. Anastasio is with the Graduate Programs in Medical Physics, Department of Radiology, The University of Chicago, Chicago, IL 60637 USA. Publisher Item Identifier S 0278 0062(99)08511 0. In computer aided diagnosis (CAD) 1] [3], computers take features extracted from medical images and determine whether pathology is present by using automated classifiers [4] 5] It is well known that the optimal method for classifying is to use the likelihood ratio or any monotonic transformation of the likelihood ratio as the ....
....Two curves may have equal values, as shown in Fig. 2. However, one of the curves will typically be preferred over the other, depending upon the relative preference of the sensitivity and the specificity needed for the task at hand. For certain types of classifiers, such as rule based systems [3], 25] it may not be clear how should be varied to sweep out the ROC curve that best represents the sensitivity specificity tradeoffs that are achievable by the classifier on the specified dataset. The ROC curves generated by varying different sets of components of will generally be different, ....
R. M. Nishikawa, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, "Computer-aided detection of clustered microcalcifications on digital mammograms," Med. Biol. Eng. Comput., vol. 33, pp. 174--178, 1995.
Statistical Segmentation of Mammograms - Comer, Liu, Delp (1996) (Correct)
....School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907, USA 1. INTRODUCTION In the past several years there has been tremendous interest in image processing and analysis techniques in mammography. One common approach for detecting abnormalities in mammograms [1, 2] is to use a series of heuristics, e.g. filtering and thresholding, which may include texture analysis to automatically detect abnormalities [3] These heuristic methods su#er from a lack of robustness when the number of images to be classified is large [4] Statistical methods have also been ....
R. M. Nishikawa, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, "Computeraided detection of clustered microcalcifications on digital mammograms," Medical and Biological Engineering and Computing, vol. 33, no. 2, pp. 174--178, March 1995.
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