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Fig. 4 | BMC Medical Imaging

Fig. 4

From: MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

Fig. 4

Performance evaluation of the standard 3D U-Net model for kidney and tumor prediction with a threefold cross-validation on the 120 CT data set from the KiTS19 challenge. Left: Tversky loss against epochs illustrating loss development during training for the corresponding test and train data sets. Each point represents the average Tversky loss between the cross-validation folds. Center: Class-wise Dice coefficient against epochs illustrating soft Dice similarity coefficient development during training for the corresponding test and train data sets. Each point represents the average soft Dice similarity coefficient between the cross-validation folds. Right: Dice similarity coefficient distribution for the kidney and tumor for all samples of the cross-validation

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