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

2023-12-01 11:06:00
Aristotelis Ballas, Vasileios Papapanagiotou, Christos Diou

Abstract

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.

Abstract (translated)

目标:尽管最近研究活动有所增加,但深度学习模型在医学界尚未得到广泛接受。高质量带标签的数据不足往往阻碍了具有稳健和泛化能力的模型的开发,这些模型在接触到新颖且分布不在预期的(OOD)数据时不会出现性能下降。方法:对比自监督学习(SSL)是一种潜在的解决方法,因为它利用未标记数据来提高模型的有效性和稳健性。在这项研究中,我们提出应用对比SSL来检测心电图(PCG)样本中的异常,通过学习信号的一般表示来完成。具体来说,我们在广泛的音频增强技术和多个数据集上进行了深入的比较评估,并在不同的下游任务上评估训练后的分类器。结果:我们通过实验证明了,根据其训练分布,完全监督模型的效果可能会降低32%,而SSL模型只会损失10%或者甚至提高。结论:对比SSL预训练可以帮助提供具有稳健性的分类器,使其能够泛化到未见过的、分布不在预期的数据中。此外,所提出的详细评估协议揭示了最有可能的和适当的增强技术,有助于对PCG信号处理实现更好的泛化。意义:我们为研究人员和实践者提供了一个走向生产具有稳健性的PCG分类模型的路线图,同时提供一个用于开发新方法的开放源代码库。

URL

https://arxiv.org/abs/2312.00502

PDF

https://arxiv.org/pdf/2312.00502.pdf


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