Interpretable Anomaly Detection for Lung Sounds Using Topology

In the medical field, research on computer-aided diagnosis using machine learning has been actively conducted. While machine learning can achieve high accuracy by collecting a large amount of data, low interpretability of machine learning is an important issue for achieving practical use in the medical field, where missing a disease may lead to fatal results. In this paper, we propose an anomaly detection method that takes the interpretability into account for diagnosing lung sounds. Furthermore, the proposed method incorporates the context information included in the sound data in the machine learning-based anomaly detection method to improve the detection performance while maintaining the interpretability of the detection results.