Objective: Despite the recent increase in research activity, deep-learning models have not yet been widely accepted in medicine. The shortage of high-quality annotated data often hinders the development of robust and generalizable models, which do not suffer from degraded effectiveness when presented with newly-collected, out-of-distribution (OOD) datasets. Methods: Contrastive Self-Supervised Learning (SSL) offers a potential solution to the scarcity of labeled data as it takes advantage of unlabeled data to increase model effectiveness and robustness. In this research, we propose applying contrastive SSL for detecting abnormalities in phonocardiogram (PCG) samples by learning a generalized representation of the signal. Specifically, we perform an extensive comparative evaluation of a wide range of audio-based augmentations and evaluate trained classifiers on multiple datasets across different downstream tasks. Results: We experimentally demonstrate that, depending on its training distribution, the effectiveness of a fully-supervised model can degrade up to 32% when evaluated on unseen data, while SSL models only lose up to 10% or even improve in some cases. Conclusions: Contrastive SSL pretraining can assist in providing robust classifiers which can generalize to unseen, OOD data, without relying on time- and labor-intensive annotation processes by medical experts. Furthermore, the proposed extensive evaluation protocol sheds light on the most promising and appropriate augmentations for robust PCG signal processing. Significance: We provide researchers and practitioners with a roadmap towards producing robust models for PCG classification, in addition to an open-source codebase for developing novel approaches.

翻译：目的：尽管近期研究活动有所增加，深度学习模型在医学领域尚未得到广泛认可。高质量标注数据的短缺常阻碍开发鲁棒且泛化能力强的模型——这类模型在面对新采集的分布外数据集时性能不会下降。方法：对比自监督学习（SSL）通过利用未标注数据提升模型效能与鲁棒性，为解决标注数据稀缺问题提供了潜在方案。本研究提出应用对比SSL检测心音（PCG）样本异常，通过学习信号的通用化表征。具体而言，我们对多种基于音频的数据增强方法开展全面比较评估，并在多个数据集上评估了不同下游任务下的分类器性能。结果：实验表明，全监督模型在未见数据上的效能最高下降32%（取决于训练数据分布），而SSL模型仅损失不超过10%，部分案例中甚至有所提升。结论：对比SSL预训练有助于构建能够泛化至未见分布外数据的鲁棒分类器，无需依赖医学专家耗时费力的标注流程。此外，本研究提出的系统评估方案揭示了实现鲁棒PCG信号处理的最具前景且最适配的数据增强方法。意义：我们为研究者及从业者提供了构建鲁棒PCG分类模型的技术路线图，并开源了代码库以支持新型方法的开发。