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Fetal standard scan plane detection during 2-D mid-pregnancy examinations isa highly complex task, which requires extensive medical knowledge and years oftraining. Although deep neural networks (DNN) can assist inexperiencedoperators in these tasks, their lack of transparency and interpretability limittheir application. Despite some researchers have been committed to visualizingthe decision process of DNN, most of them only focus on the pixel-levelfeatures and do not take into account the medical prior knowledge. In thiswork, 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 toconstruct the relationships between key medical concepts. Extensiveexperimental analysis on a private dataset has shown that the proposed methodprovides easy-to-understand insights about reasoning results for clinicians.

A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts