Figure 5—figure supplement 5.

To asses the reliability and reproducibility of the consensus models and consensus ensembles strategies, we illustrate the within group MF1 score at a matching IoU-threshold of t=0.5 and the within group M¯IoU across different datasets. Color coding refers to the different DL strategies: blue to consensus models; orange to consensus ensembles. All images used for training were excluded from the analysis. The differences between the datasets highlight the difficulties in establishing a unified approach for automated fluorescent label annotation. (A) The Lab-Mue analysis comprises n = 24 images and the following models: N_{consensus models} = 12 and N_{consensus ensembles} = 3 for all initialization variants. (B) The Lab-Inns1 analysis comprises n = 20 images and the following models: N_{from scratch consensus models} = 6, N_{fine-tuned consensus models} = 8, N_{frozen consensus models} = 12, and N_{consensus ensembles} = 3 for all initialization variants. (C) The Lab-Inns2 analysis comprises n = 25 images and the following models: N_{from scratch consensus models} = 15, N_{fine-tuned consensus models} = 15, N_{frozen consensus models} = 12, and N_{consensus ensembles} = 3 for all initialization variants. (D) The Lab-Wue2 analysis comprises n = 25 images and the following models: N_{from scratch consensus models} = 15, N_{fine-tuned consensus models} = 15, N_{frozen consensus models} = 12, and N_{consensus ensembles} = 3 for each initialization variant.