Table 3.

Comparison of dice scores between training and test sets.

Models and inputs	Dice score in training setMedian [IQR]	Dice score in test setMedian [IQR]
Tmax-only models
U-Net	0.52 [0.24–0.69]	0.48 [0.18–0.68]
Random forests	0.58 [0.37–0.71]	0.51 [0.27–0.66]
Gradient boosting	0.57 [0.33–0.71]	0.53 [0.29–0.68]
Extended-perfusion models
U-Net	0.50 [0.19–0.69]	0.48 [0.17–0.68]
Random Forests	0.61 [0.42–0.73]	0.52 [0.28–0.67]
Gradient boosting	0.58 [0.37–0.71]	0.54 [0.31–0.68]

Note: Metrics were compared between training and test sets. Random Forests were slightly more prone to overfitting as compared to Gradient Boosting and U-Net, with higher Dice Scores in the training set than in the test set. IQR: Interquartile Range.