论文标题
不确定性意识的深度学习方法用于鲁棒性糖尿病性视网膜病变分类
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
论文作者
论文摘要
通过深层神经网络对视网膜图像的糖尿病性视网膜病的自动分类已被广泛研究，结果令人印象深刻。但是，临床需要估计分类中的不确定性，这是现代神经网络的缺点。最近，已经提出了该任务的近似贝叶斯深度学习方法，但研究仅考虑了应用于基准数据集的二元引用/不可引用的糖尿病性视网膜病变分类。除基准数据集和二进制分类方案外，我们还通过系统地研究临床数据集和临床相关的5级分类方案来介绍新的结果。此外，我们得出了不确定性度量与分类器风险之间的联系，从中我们开发了一种新的不确定性度量。我们观察到，先前提出的基于熵的不确定性度量概括了二进制分类方案的临床数据集，而不是在5级方案上，而我们的新不确定性量度则概括为后一种情况。
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results. However, there is a clinical need for estimation of the uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian deep learning methods have been proposed for the task but the studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results by systematically investigating a clinical dataset and a clinically relevant 5-class classification scheme, in addition to benchmark datasets and the binary classification scheme. Moreover, we derive a connection between uncertainty measures and classifier risk, from which we develop a new uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure generalizes to the clinical dataset on the binary classification scheme but not on the 5-class scheme, whereas our new uncertainty measure generalizes to the latter case.
