Despite the recent increase in research activity, deep-learning models have not yet been widely accepted in several real-world settings, such as 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 out-of-distribution (OOD) datasets. Contrastive Self-Supervised Learning (SSL) offers a potential solution to labeled data scarcity, as it takes advantage of unlabeled data to increase model effectiveness and robustness. However, the selection of appropriate transformations during the learning process is not a trivial task and even breaks down the ability of the network to extract meaningful information. In this research, we propose uncovering the optimal augmentations for applying contrastive learning in 1D phonocardiogram (PCG) classification. We perform an extensive comparative evaluation of a wide range of audio-based augmentations, evaluate models on multiple datasets across downstream tasks, and report on the impact of each augmentation. We demonstrate that depending on its training distribution, the effectiveness of a fully-supervised model can degrade up to 32%, while SSL models only lose up to 10% or even improve in some cases. We argue and experimentally demonstrate that, 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 evaluation protocol sheds light on the most promising and appropriate augmentations for robust PCG signal processing, by calculating their effect size on model training. Finally, 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：尽管近期研究活动有所增加，但深度学习模型尚未在医学等实际场景中得到广泛接受。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——这些模型在面对分布外数据集时不会出现效果退化。对比自监督学习为应对标注数据稀缺提供了潜在解决方案，其通过利用无标注数据提升模型效果与鲁棒性。然而，学习过程中选择恰当的数据变换并非易事，甚至可能削弱网络提取有意义信息的能力。本研究旨在揭示一维心音图分类中应用对比学习的最优数据增强策略。我们对多种基于音频的增强方法进行了广泛比较评估，在多个数据集上跨下游任务测试模型，并报告每种增强方法的影响。研究表明：取决于训练数据分布，全监督模型的效果最多可下降32%，而自监督模型仅损失不超过10%甚至在某些情况下有所提升。我们通过实验论证：对比自监督预训练有助于构建鲁棒分类器，使其能够泛化至未见过的分布外数据，且无需依赖医学专家耗费大量时间与精力的标注过程。此外，所提出的评估协议通过计算增强方法对模型训练的效果量，揭示了最适用于鲁棒心音图信号处理的增强方案。最后，我们为研究人员和从业者提供了一条构建鲁棒心音图分类模型的技术路线图，并开源了用于开发创新方法的代码库。