论文标题
标签不确定性引导的疾病筛查多流模型
Label uncertainty-guided multi-stream model for disease screening
论文作者
论文摘要
医疗图像数据集的疾病严重程度的注释通常依赖于多个人类分级的协作决策。从个体差异得出的观察者内部变异性在此过程中始终存在，但影响通常被低估了。在本文中，我们将观察者内变异性视为不确定性问题，并将标签不确定性信息作为疾病筛查模型的指导，以改善最终决定。主要思想是将图像通过不确定性信息将图像分为简单而硬的情况，然后开发一个多流网络以分别处理不同的情况。特别是，对于严重的情况，我们可以增强网络在捕获正确的疾病特征和抵抗不确定性干扰方面的能力。基于眼底图像的青光眼筛查案例研究的实验表明，该提出的模型的表现优于几个基准，尤其是在筛选硬病例中。
The annotation of disease severity for medical image datasets often relies on collaborative decisions from multiple human graders. The intra-observer variability derived from individual differences always persists in this process, yet the influence is often underestimated. In this paper, we cast the intra-observer variability as an uncertainty problem and incorporate the label uncertainty information as guidance into the disease screening model to improve the final decision. The main idea is dividing the images into simple and hard cases by uncertainty information, and then developing a multi-stream network to deal with different cases separately. Particularly, for hard cases, we strengthen the network's capacity in capturing the correct disease features and resisting the interference of uncertainty. Experiments on a fundus image-based glaucoma screening case study show that the proposed model outperforms several baselines, especially in screening hard cases.
