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For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address(More)
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the(More)
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this problem when annotators may be unreliable (labels are noisy), but also their expertise varies depending on the data they observe (annotators may have knowledge about different parts(More)
PURPOSE To develop a bilateral coil and fat suppressed T1-weighted sequence for 7 Tesla (T) breast MRI. MATERIALS AND METHODS A dual-solenoid coil and three-dimensional (3D) T1w gradient echo sequence with B1+ insensitive fat suppression (FS) were developed. T1w FS image quality was characterized through image uniformity and fat-water contrast(More)
Purpose. In November 2009, the U.S. Preventative Service Task Force (USPSTF) revised their breast cancer screening guidelines. We evaluated the pattern of screening subsequent to the altered guidelines in a cohort of women. Methods. Our database was queried for the following variables: age, race, method of diagnosis, mass palpability, screening frequency,(More)
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