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.
@article{arxiv.2002.09809,
title = {Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization},
author = {Darvin Yi and Endre Grøvik and Michael Iv and Elizabeth Tong and Greg Zaharchuk and Daniel Rubin},
journal= {arXiv preprint arXiv:2002.09809},
year = {2020}
}