Similar to use case 1, we implemented our foundation model on use case 2 and compared it against baseline methods using the mAP. Both metrics were computed when trained on the full and 50, 20 and 10% of the dataset. In e , f , Models Genesis approaches are shaded and/or dotted as they were trained on the same data split of LUNA16 and therefore do not present a fair comparison due to overfitting. For use case 2, we also added a supervised model fine-tuned through transfer learning from use case 1. The error bars for a – f show 95% CIs of the estimates and the bar centre shows the mean estimate of the displayed metric. The estimates were computed by generating a bootstrap distribution with 1,000 resamples for datasets with n = 1,221 samples ( a – d ) and n = 170 samples ( e , f ).
