Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
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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 out. We also apply a lopsided bootstrap loss to recover performance after inducing an in silico 50 sensitive. We improve our network detection of lesions's mAP value by 39 more than triple the sensitivity at 80 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.
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