论文标题
用于呼吸声中异常检测的变异自动编码器
Variational Autoencoders for Anomaly Detection in Respiratory Sounds
论文作者
论文摘要
本文提出了一种基于机器学习的方法，旨在提醒患者可能呼吸疾病的工具。各种类型的病理可能会影响呼吸系统，可能导致严重的疾病，在某些情况下死亡。通常，有效的预防实践被视为改善患者健康状况的主要参与者。提出的方法致力于实现一种易于使用的工具，以自动诊断呼吸道疾病。具体而言，该方法利用了各种自动编码器体系结构，允许使用有限的复杂性和相对较小的数据集的培训管道。重要的是，它的精度为57％，这符合现有的强烈监督方法。
This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57 %, which is in line with the existing strongly-supervised approaches.
