Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases. As of late, they have also established themselves in the biomedical and healthcare domains, where they have shown a lot of promise, but have not yet achieved widespread adoption. This is in part due to the fact that most methods fail to maintain their performance when they are called to make decisions on data that originate from a different distribution than the one they were trained on, namely Out-Of-Distribution (OOD) data. For example, in the case of biosignal classification, models often fail to generalize well on datasets from different hospitals, due to the distribution discrepancy amongst different sources of data. Our goal is to demonstrate the Domain Generalization problem present between distinct hospital databases and propose a method that classifies abnormalities on 12-lead Electrocardiograms (ECGs), by leveraging information extracted across the architecture of a Deep Neural Network, and capturing the underlying structure of the signal. To this end, we adopt a ResNet-18 as the backbone model and extract features from several intermediate convolutional layers of the network. To evaluate our method, we adopt publicly available ECG datasets from four sources and handle them as separate domains. To simulate the distributional shift present in real-world settings, we train our model on a subset of the domains and leave-out the remaining ones. We then evaluate our model both on the data present at training time (intra-distribution) and the held-out data (out-of-distribution), achieving promising results and surpassing the baseline of a vanilla Residual Network in most of the cases.
翻译:深海学习系统在过去几年里取得了巨大成功,甚至在若干情况下甚至超过了人类智能。最近,它们还在生物医学和医疗领域建立了自我定位,它们在这方面表现出了很大的希望,但还没有被广泛采用。部分原因是,大多数方法未能保持其性能,因为大多数方法在被要求对源自与它们所培训的数据,即“Out-OD(OOOD)”数据的不同分布数据作出决定时,无法保持其性能。例如,在生物信号分类方面,由于不同数据来源之间的分布差异,模型往往无法对不同医院的数据集进行概括化。我们的目标是展示不同医院数据库之间存在的多功能化问题,并提出一种方法,通过利用从深神经网络结构中提取的信息,即“ODOD(OD)”数据,并获取信号的基本结构。为此,我们采用了ResNet-18(ResNet-18)作为离线模型的基础模型,并提取了来自不同医院的中间剖析层数据,因为不同数据来源之间的分布差异。我们的目标是,为了评估不同的医院数据库之间存在的多层次,我们采用一种可公开使用的方法,在目前使用的ECG数据流数据流数据流数据流数据流数据流数据流数据流中,我们在数据库中,我们使用一种可操作。