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Figure 5—figure supplement 2. Performance of consensus ensembles on fluorescent feature annotation in image dataset Lab-Inns02.

(A) Representative microscopy images. Orange: representative annotations of a lab-specific from scratch consensus ensemble. Resp: responders, nResp: non-responders. Scale bar: 40 µm. (B) Mean MF1 score of from scratch (solid line) and fine-tuned (dashed line) consensus models on the validation dataset over the course of training (iterations). Mean MF1 score of frozen consensus models are indicated with an arrow. Box plots show the MF1 score among the annotations of human experts as reference and the mean MF1 score of selected consensus models. Two dotted horizontal lines mark the whisker ends of the MF1 score among the human expert annotations. (C–E) Effect sizes of all individual bioimage analyses (black: manual experts, blue: consensus models, orange: consensus ensembles). Three horizontal lines separate four selected significance intervals (n.s.: not significant, *: 0.05 ≥ p>0.01, **: 0.01 ≥ p>0.001, ***: p ≤ 0.001 after Bonferroni correction for multiple comparisons). The analyses were performed with: Nconsensus ensembles = 3 for each initialization variant; Nfrom scratch consensus models = 15 (for each ensemble, 5/5 trained models per ensemble met the selection criterion), Nfine-tuned consensus models = 15, (for each ensemble, 5/5 trained models per ensemble met the selection criterion), Nfrozen consensus models = 12 (for each ensemble, 4/4 models per ensemble did not meet the selection criterion). Thus, the frozen consensus model and ensemble results need to be considered with caution. Nsaline = 6, NL-DOPA/MS-275 Resp = 6, NL-DOPA/MS-275 nResp = 3; nsaline = 10, nL-DOPA/MS-275 Resp = 10, nL-DOPA/MS-275 nResp = 5. Statistical data can be found in Figure 5—source data 1. Source files on model performance and selection are available in (Figure 5—source data 3).

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