arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.12089cs.CV
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基于标记不确定性的疾病筛查多流模型

Label uncertainty-guided multi-stream model for disease screening

Zhongshan Ophthalmic Center, Sun Yat-sen University(中山大学中山眼科中心);Monash eResearch Centre, Monash University(蒙纳士大学电子研究中心);Centre for Eye Research, Melbourne University(墨尔本大学眼科研究中心)

Chi Liu, Zongyuan Ge, Mingguang He, Xiaotong Han

摘要:

医学图像数据集的疾病严重程度的注释通常依赖于来自多个人类分级者的协作决策。个体差异导致的观察者内变异性始终存在于这一过程中,但其影响往往被低估。在本文中,我们将观察者内变异性视为一个不确定性问题,并将标签不确定性信息作为指导纳入疾病筛查模型,以改善最终决策。其主要思想是根据图像的不确定性信息将图像分为简单和困难两种情况,然后开发一个多流网络来分别处理不同的情况。特别地,对于困难的情况,我们加强了网络捕捉正确疾病特征和抵抗不确定性干扰的能力。基于眼底图像的青光眼筛查案例研究的实验表明,该模型优于几个基线,特别是在筛选困难的情况下。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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