论文标题
基于医学概念的胎儿超声图像分类器的认知解释器
A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts
论文作者
论文摘要
在2D中期检查期间，胎儿标准扫描平面检测是一项高度复杂的任务，需要广泛的医学知识和多年的培训。尽管深度神经网络（DNN）可以帮助经验不足的操作员执行这些任务，但他们缺乏透明度和解释性限制了其应用。尽管一些研究人员一直致力于可视化DNN的决策过程，但其中大多数仅专注于像素级功能，并且不考虑医学的先验知识。在这项工作中，我们提出了一个基于关键医学概念的可解释框架，该框架从临床医生认知的角度提供了解释。此外，我们利用基于概念的图形卷积神经（GCN）网络来构建关键医学概念之间的关系。对私人数据集的广泛实验分析表明，所提出的方法为临床医生的推理结果提供了易于理解的见解。
Fetal standard scan plane detection during 2-D mid-pregnancy examinations is a highly complex task, which requires extensive medical knowledge and years of training. Although deep neural networks (DNN) can assist inexperienced operators in these tasks, their lack of transparency and interpretability limit their application. Despite some researchers have been committed to visualizing the decision process of DNN, most of them only focus on the pixel-level features and do not take into account the medical prior knowledge. In this work, we propose an interpretable framework based on key medical concepts, which provides explanations from the perspective of clinicians' cognition. Moreover, we utilize a concept-based graph convolutional neural(GCN) network to construct the relationships between key medical concepts. Extensive experimental analysis on a private dataset has shown that the proposed method provides easy-to-understand insights about reasoning results for clinicians.
