论文标题
随机束：通过注释随机化结合脑转移分割结合
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
论文作者
论文摘要
我们介绍了一种新型的结合方法，即随机束（RB），可改善脑转移分割的性能。我们通过在数据集中训练每个网络，以50％的注释病变进行审查来创建合奏。在诱导硅50％的假负率中，我们还应用了偏斜的自举损失，以恢复性能，并使我们的网络更加敏感。我们以80％的精度将病变的MAP值的网络检测提高了39％，而敏感性的三倍以上。我们还通过骰子得分显示了细分质量的略有改进。此外，与各种流行的结合策略相比，RB结合效果可以提高基线的性能。最后，我们表明，当将两个系统限制为具有相同的计算时，RB Enembly通过将其性能与单个网络进行比较，在计算上是有效的。
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.
