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Objective: Despite the recent increase in research activity, deep-learningmodels have not yet been widely accepted in medicine. The shortage ofhigh-quality annotated data often hinders the development of robust andgeneralizable models, which do not suffer from degraded effectiveness whenpresented with newly-collected, out-of-distribution (OOD) datasets. Methods:Contrastive Self-Supervised Learning (SSL) offers a potential solution to thescarcity of labeled data as it takes advantage of unlabeled data to increasemodel effectiveness and robustness. In this research, we propose applyingcontrastive SSL for detecting abnormalities in phonocardiogram (PCG) samples bylearning a generalized representation of the signal. Specifically, we performan extensive comparative evaluation of a wide range of audio-basedaugmentations and evaluate trained classifiers on multiple datasets acrossdifferent downstream tasks. Results: We experimentally demonstrate that,depending on its training distribution, the effectiveness of a fully-supervisedmodel can degrade up to 32% when evaluated on unseen data, while SSL modelsonly lose up to 10% or even improve in some cases. Conclusions: Contrastive SSLpretraining can assist in providing robust classifiers which can generalize tounseen, OOD data, without relying on time- and labor-intensive annotationprocesses by medical experts. Furthermore, the proposed extensive evaluationprotocol sheds light on the most promising and appropriate augmentations forrobust PCG signal processing. Significance: We provide researchers andpractitioners with a roadmap towards producing robust models for PCGclassification, in addition to an open-source codebase for developing novelapproaches.

On the Out-Of-Distribution Robustness of Self-Supervised Representation Learning for Phonocardiogram Signals