Figure 2—figure supplement 2.

To determine an appropriate size for the consensus ensembles, we analyzed the homogeneity of the results through a similarity analysis. Therefore, we calculated the M_{F1 score} at an IoU matching threshold of t=0.5 for each ensemble size i∈{1,…,10} on the holdout test set (n = 9 images). Stratified on the cross validation splits, we randomly sampled the ensembles from a collection of trained consensus models. We repeated this procedure five times to mitigate the random effect of the ensemble composition (N_ensembles = 5 for each i). The blue box (i=1) depicts the variability between different consensus models. The orange box (i=4) shows the variability of the finally chosen size for the consensus ensembles, as no substantial reduction in variation can be observed for larger i. In addition, i=4 corresponds to the number of cross validation splits (k=4), meaning that the ensembles have seen the entire training set. The black line denotes the standard deviation of M_{F1 score}, which is scaled at the right y-Axis. The dashed black line denotes the best fitting function of type ƒ(x)=(a /√x) with a=0.096 for the standard deviation.