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Fig. 8 | BMC Bioinformatics

Fig. 8

From: Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images

Fig. 8

Model testing performance metrics: testing accuracy and area under receiver operating characteristic curve. Testing accuracy (above) and ROC AUC (below) for the 44 tests. The four different symbol shapes/colors correspond to the four networks. Solid and hollow symbols represent non-augmented and augmented test sets, respectively. Error bars are binomial exact 95% confidence intervals. Horizontal axes labels stand for different ways of applying augmentation: A = Make three sets, then augment validation; B = Make three sets; C = Make three sets, then augment training; D = Make three sets, then augment both training and validation; E = Separate test set, augment the rest, then make two other sets; F = Augment all before making three sets. CI = confidence interval; ROC AUC = area under the receiver operating characteristic curve

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