Interpretable Anomaly Detection for Lung Sounds Using Topology
Interpretable Anomaly Detection for Lung Sounds Using Topology
复制标题
DOI：
10.1109/icaiic57133.2023.10067072
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Ryosuke Wakamoto;Shingo Mabu
中科院分区：
文献类型：
--
作者：
Ryosuke Wakamoto;Shingo Mabu
在医学领域，已经积极地进行了使用机器学习的计算机辅助诊断的研究。虽然机器学习可以通过收集大量数据来实现高准确性，但机器学习的低可解释性是在医疗领域实现实际应用的重要问题，其中错过疾病可能导致致命的结果。在本文中，我们提出了一个异常检测方法，考虑到诊断肺音的可解释性。此外，所提出的方法将声音数据中包含的上下文信息结合到基于机器学习的异常检测方法中，以提高检测性能，同时保持检测结果的可解释性。
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.