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We introduce a novel ensembling method, Random Bundle (RB), that improvesperformance for brain metastases segmentation. We create our ensemble bytraining each network on our dataset with 50% of our annotated lesions censoredout. We also apply a lopsided bootstrap loss to recover performance afterinducing an in silico 50% false negative rate and make our networks moresensitive. We improve our network detection of lesions's mAP value by 39% andmore than triple the sensitivity at 80% precision. We also show slightimprovements in segmentation quality through DICE score. Further, RB ensemblingimproves performance over baseline by a larger margin than a variety of popularensembling strategies. Finally, we show that RB ensembling is computationallyefficient by comparing its performance to a single network when both systemsare constrained to have the same compute.

Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization