Application of deep neural networks to medical imaging tasks has in some sense become commonplace. Still, a "thorn in the side" of the deep learning movement is the argument that deep networks are prone to overfitting and are thus unable to generalize well when datasets are small (as is common in medical imaging tasks). One way to bolster confidence is to provide mathematical guarantees, or bounds, on network performance after training which explicitly quantify the possibility of overfitting. In this work, we explore recent advances using the PAC-Bayesian framework to provide bounds on generalization error for large (stochastic) networks. While previous efforts focus on classification in larger natural image datasets (e.g., MNIST and CIFAR-10), we apply these techniques to both classification and segmentation in a smaller medical imagining dataset: the ISIC 2018 challenge set. We observe the resultant bounds are competitive compared to a simpler baseline, while also being more explainable and alleviating the need for holdout sets.
翻译:深神经网络应用于医学成像任务在某种意义上已经变得司空见惯了。然而,深神经网络在深学习运动的一面“一角”的论据是,深网络容易过度装配,因此当数据集小时无法全面推广(在医学成像任务中司空见惯)。 增强信心的一种方法是在培训后提供网络性能的数学保障或界限,培训后明确量化了超配的可能性。 在这项工作中,我们探索了最近的进展,利用PAC-Bayesian框架为大型(随机)网络提供通用错误的界限。尽管以前的努力侧重于较大自然图像数据集的分类(如MNIST和CIFAR-10),但我们将这些技术应用于小医学想象数据集中的分类和分解:ISIC 2018挑战集。我们观察结果的界限与更简单的基线相比是竞争性的,同时更加可以解释,并减轻对屏蔽装置的需求。