Label scarcity problem is the main challenge that hinders the wide application of deep learning systems in automatic cardiovascular diseases (CVDs) detection using electrocardiography (ECG). Tuning pre-trained models alleviates this problem by transferring knowledge learned from large datasets to downstream small datasets. However, bottlenecks in computational efficiency and CVDs detection performance limit its clinical applications. It is difficult to improve the detection performance without significantly sacrificing model computational efficiency. Here, we propose a computation-efficient semi-supervised learning paradigm (FastECG) for robust and computation-efficient CVDs detection using ECG. It enables a robust adaptation of pre-trained models on downstream datasets with limited supervision and high computational efficiency. First, a random-deactivation technique is developed to achieve robust and fast low-rank adaptation of pre-trained weights. Subsequently, we propose a one-shot rank allocation module to determine the optimal ranks for the update matrices of the pre-trained weights. Finally, a lightweight semi-supervised learning pipeline is introduced to enhance model performance by leveraging labeled and unlabeled data with high computational efficiency. Extensive experiments on four downstream ECG datasets demonstrate that FastECG not only outperforms the state-of-the-art methods in multi-label CVDs detection but also consumes fewer GPU footprints, training time, and parameter storage space. As such, this paradigm provides an effective solution for achieving high computational efficiency and robust detection performance in the clinical applications of pre-trained models under limited supervision.

翻译：标签稀缺问题是阻碍深度学习系统在心电图（ECG）自动心血管疾病（CVD）检测中广泛应用的主要挑战。通过微调预训练模型，可将从大型数据集学到的知识迁移至下游小型数据集，从而缓解此问题。然而，计算效率与CVD检测性能的瓶颈限制了其临床应用。在不显著牺牲模型计算效率的前提下提升检测性能十分困难。本文提出一种计算高效的半监督学习范式（FastECG），用于实现鲁棒且计算高效的ECG心血管疾病检测。该范式能够在有限监督和高计算效率条件下，实现预训练模型在下游数据集上的鲁棒自适应。首先，我们开发了一种随机失活技术，以实现预训练权重的鲁棒快速低秩自适应。随后，提出一次性秩分配模块来确定预训练权重更新矩阵的最优秩。最后，引入轻量级半监督学习流程，通过高效利用标注与未标注数据以提升模型性能。在四个下游ECG数据集上的大量实验表明，FastECG不仅在多标签CVD检测任务上优于现有先进方法，同时消耗更少的GPU内存占用、训练时间与参数存储空间。因此，该范式为在有限监督条件下实现预训练模型临床应用的高计算效率与鲁棒检测性能提供了有效解决方案。