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.

应用对比自监督学习方法，以及音频增强技术，通过学习泛化的心音图信号表示，检测心音图样本中的异常情况，并在多个数据集上进行了广泛的评估，证实了对比自监督学习方法可以提供具有鲁棒性的分类器，能够在未见过的数据上进行泛化，而无需专家进行耗时耗力的注释过程。