  We introduce a novel ensembling method, Random Bundle (RB), that improves
performance for brain metastases segmentation. We create our ensemble by
training each network on our dataset with 50% of our annotated lesions censored
out. We also apply a lopsided bootstrap loss to recover performance after
inducing an in silico 50% false negative rate and make our networks more
sensitive. We improve our network detection of lesions's mAP value by 39% and
more than triple the sensitivity at 80% precision. We also show slight
improvements in segmentation quality through DICE score. Further, RB ensembling
improves performance over baseline by a larger margin than a variety of popular
ensembling strategies. Finally, we show that RB ensembling is computationally
efficient by comparing its performance to a single network when both systems
are constrained to have the same compute.
