Fig. 6

Ensembles improve performance and are robust to false information. a Five trained individual-expert CNNs, combined by a trainable sparse affine layer, make up an ensemble model. The training process simply determines how to best weigh and combine each CNN’s existing class predictions. b Ensembling on average increases performance for each Aβ class, and for both consensus and individual benchmarks. Performance gains are calculated by averaging each ensemble’s AUPRC on the hold-out test set minus the corresponding individual-expert CNN’s AUPRC on the same set, across all ten benchmarks (Methods). c We tested ensembling with a random labeler CNN, trained using a randomly shuffled permutation of labels with the same class distribution ratios as the five expert annotations. d Ensemble performance is largely unaffected by inclusion of a random labeler CNN. Density histogram of AUPRC performance differences for each Aβ class between the normal ensemble and the ensemble with a single random labeler CNN. Each ensemble is evaluated on all ten benchmarks (five individual-expert benchmarks, five consensus benchmarks), and the absolute value of the performance differential (x-axis) is calculated and binned for each class. e Ensemble architecture with multiple random labeler CNNs, each trained on a different permutation of randomly shuffled labels. f Ensemble performance is largely unaffected by inclusion of five random labeler CNNs. Same density histogram as in d, but comparison is between normal ensemble and ensemble with five random labeler CNNs injected