  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.
