论文标题
使用元数据和对比度学习预处理呼吸道声音
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
论文作者
论文摘要
基于监督学习的方法使用端到端的注释是分类问题的最新方法。但是，它们的概括能力可能受到限制，尤其是在低数据状态下。在这项研究中，我们使用有监督的对比学习与可用元数据相结合来解决此问题，以解决多个借口任务，以学习良好的数据表示。我们将方法应用于呼吸道声音分类。此任务适合这种设置，因为性别和年龄等人口统计信息与肺部疾病的存在相关，并且学习隐式编码此信息的系统可能会更好地检测异常情况。监督的对比学习是一种范式，它与样本相似的表示形式与具有不同类标签的样本共享相同的类标签和不同表示形式。功能提取器使用此范式从数据中汲取有用的特征学习，我们表明它在两个不同数据集中对呼吸异常进行分类时表现优于跨凝结。我们还表明，仅使用没有类标签的元数据的学习表示形式与仅使用这些标签使用交叉熵的性能相似。此外，当使用多个监督的对比学习将类标签与元数据结合在一起时，延伸了有监督的对比度学习，解决了将患者分组在同一性别和年龄组中的其他任务，就会学习更多信息。这项工作表明，在有监督的对比度设置中使用多个元数据源，尤其是在具有类不平衡的设置和很少的数据中。我们的代码在https://github.com/ilyassmoummad/scl_icbhi2017上发布
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combined with available metadata to solve multiple pretext tasks that learn a good representation of data. We apply our approach on respiratory sound classification. This task is suited for this setting as demographic information such as sex and age are correlated with presence of lung diseases, and learning a system that implicitly encode this information may better detect anomalies. Supervised contrastive learning is a paradigm that learns similar representations to samples sharing the same class labels and dissimilar representations to samples with different class labels. The feature extractor learned using this paradigm extract useful features from the data, and we show that it outperforms cross-entropy in classifying respiratory anomalies in two different datasets. We also show that learning representations using only metadata, without class labels, obtains similar performance as using cross entropy with those labels only. In addition, when combining class labels with metadata using multiple supervised contrastive learning, an extension of supervised contrastive learning solving an additional task of grouping patients within the same sex and age group, more informative features are learned. This work suggests the potential of using multiple metadata sources in supervised contrastive settings, in particular in settings with class imbalance and few data. Our code is released at https://github.com/ilyassmoummad/scl_icbhi2017
