论文标题
广泛的WASSERSTEIN DICE评分，分布强大的深度学习和脑肿瘤分割的游侠：Brats 2020挑战
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge
论文作者
论文摘要
训练深层神经网络是一个主要成分的优化问题：深神经网络的设计，样本损耗函数，人口损失函数和优化器。但是，开发出来在最近的小型挑战中竞争的方法往往只关注深度神经网络体系结构的设计，同时更少关注其他三个方面。在本文中，我们尝试采用相反的方法。我们坚持使用通用和最先进的3D U-NET架构，并尝试了非标准的每样本损耗函数，广义的Wasserstein骰子损失，非标准人口损失函数，对应于分布强劲的优化和非标准优化器，Ranger，Ranger。这些变化是专门为多类脑肿瘤分割的问题而选择的。广义的瓦斯汀骰子丢失是每样本样本损耗函数，它允许利用蝙蝠标记的肿瘤区域的分层结构。分布鲁棒的优化是经验风险最小化的概括，这解释了培训数据集中代表性不足的子域的存在。 Ranger是对广泛使用的ADAM优化器的概括，具有小批量尺寸和嘈杂的标签更稳定。我们发现，对脑肿瘤分割的深神经网络优化的每种变化都会在骰子评分和Hausdorff距离方面得到改善。通过三个深层神经网络的合奏，通过各种优化程序训练，我们在Brats 2020挑战的验证数据集上取得了令人鼓舞的结果。我们的合奏在693支注册团队中排名第四，以完成2020挑战的小组的细分任务。
Training a deep neural network is an optimization problem with four main ingredients: the design of the deep neural network, the per-sample loss function, the population loss function, and the optimizer. However, methods developed to compete in recent BraTS challenges tend to focus only on the design of deep neural network architectures, while paying less attention to the three other aspects. In this paper, we experimented with adopting the opposite approach. We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a non-standard population loss function, corresponding to distributionally robust optimization, and a non-standard optimizer, Ranger. Those variations were selected specifically for the problem of multi-class brain tumor segmentation. The generalized Wasserstein Dice loss is a per-sample loss function that allows taking advantage of the hierarchical structure of the tumor regions labeled in BraTS. Distributionally robust optimization is a generalization of empirical risk minimization that accounts for the presence of underrepresented subdomains in the training dataset. Ranger is a generalization of the widely used Adam optimizer that is more stable with small batch size and noisy labels. We found that each of those variations of the optimization of deep neural networks for brain tumor segmentation leads to improvements in terms of Dice scores and Hausdorff distances. With an ensemble of three deep neural networks trained with various optimization procedures, we achieved promising results on the validation dataset of the BraTS 2020 challenge. Our ensemble ranked fourth out of the 693 registered teams for the segmentation task of the BraTS 2020 challenge.
