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

TL;DR

This paper introduces an interpretable framework for fetal ultrasound image classification that incorporates medical concepts and relationships, enhancing transparency and aiding clinicians' understanding.

Contribution

It proposes a novel concept-based GCN model that integrates medical prior knowledge for more interpretable fetal ultrasound classification.

Findings

Provides clear reasoning insights for clinicians

Outperforms pixel-based visualization methods

Enhances interpretability of deep neural networks

Abstract

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.