The power of machine learning in prostatecancer imaging
B). The 1000th step was predicted with a DSC of 0.9574 and an accuracy of 0.9945. The 3000th step was predicted with a DSC of 0.9991 and an accuracy of 0.9988. Although the model over-fit the training data, we still saw an increase in the prediction accuracy on synthetic images with more epochs of training and decided to use the 3000th step model for this analysis.
. The accuracy for groups were 62%, 55%, and 53%, respectively. A Pearson’s Chi-square test showed no significant association between experience level and the number of correct predictions , no association between experience level and the number of false negatives , and no association between experience level and the number of false positives ; however, a significant association was found for concordance within groups when all groups were considered.
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