改进人源数据集一致性的半强化算法:关于呼吸系统疾病分类的COVID-19案例研究 (A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification)

Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data, as it was done to generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians to diagnose the underlying diseases present in a limited number of uploaded recordings. However, this approach suffers from potential mislabeling of the coughs, as well as notable disagreement between experts. In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification. First, we leverage existing SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classification models. The consistency of the re-labeled data is demonstrated in that it exhibits a high degree of class separability, 3x higher than that of the user-labeled data, despite the expert label inconsistency present in the original dataset. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs, which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a cough classifier. This SSL approach can be used to combine the medical knowledge of several experts to improve the database consistency for any diagnostic classification task.

翻译：COUGHVID 数据元件分类是检查呼吸系统疾病的潜在有用工具,如COVID-19。由于从这种传染病的病人那里收集数据很危险,许多研究团队转而采用众包方式迅速收集咳嗽声学数据,因为这样做是为了生成COUGHVID数据集。COUGHVID 数据集的专家医生用来诊断在数量有限的上传记录中存在的潜在疾病。然而,这种方法可能存在咳嗽错误标签,以及专家之间的明显分歧。在这项工作中,我们使用半监督的学习(SSSL)方法来改进COUGHVID数据集的标签一致性以及COVID-19相对于健康咳嗽声音分类的稳健性。首先,我们利用现有的SLF专家汇总知识集技术来克服在数据集中的标签不一致性和偏松散性。我们的SSLF任务组方法用来识别一个重新标签的COUGHVI 音频样本的子样本,用来培训或增强未来咳嗽分类模型。这一重新标签的准确性数据系的一致性,它用来解释目前数据分类中的数值分类中的数值变异性,它所使用的数据值是高的数值,它用来显示原标签中所使用的数据流数据的透明性。