Fig. 3

Heatmaps for validation measures of deep learning-based models. The heatmaps show validation measures for all deep learning-based models, with precision, sensitivity and F1-score (rows) for each convolutional neural network (CNN) (column) calculated as an average of all regions of interest and all three validators. These values represent therefore the performance of the model compared to the ground truth (i.e. all external validators). Key: False positives (FP), false negatives (FN), precision (TP/[TP + FP]), sensitivity (TP/ [TP + FN]) and F1-score (2 × Precision x Sensitivity/[Precision + Sensitivity])