The presented physical breast phantoms and their matching virtual breast phantoms offer realistic breast anatomy, patient variability, and ease of use, making them a potential candidate for performing both system quality control testing and virtual clinical trials.
Receiver operating characteristics (ROC's) for the classic problem of detecting the presence or absence of one of M orthogonal signals is presented. Previous results were valid for low detectability, for which the ROC is approximately normal (i.e., appears as a straight line with unit slope on normal normal probability paper) and the detectability depends on the logarithm of the number of possible signals M. For high detectability, however, the ROC departs from normality. In addition, the rate at which detectability decreases as M increases is more rapid than that predicted by the classical approximation.
The a posteriori source position probability density function for a narrow-band source in an uncertain acoustic environment is derived. The implementation of this probability density function (pdf) is termed the optimum uncertain field processor (OUFP). It is shown that the OUFP is a generalization of matched-field processing to situations in which there is uncertainty about the environment. The robustness of the OUFP is illustrated through performance comparisons to a matched-field algorithm.
As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.
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