The 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 detection performance limit its clinical applications. It is difficult to improve the detection performance without significantly sacrificing the computational efficiency during model training. Here, we propose a computation-efficient semi-supervised learning paradigm (CE-SSL) 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 datasets demonstrate that CE-SSL 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. Code and Supplementary Materials are available at https://github.com/KAZABANA/CE-SSL

翻译：标签稀缺问题是阻碍深度学习系统在心电图（ECG）心血管疾病（CVD）自动检测中广泛应用的主要挑战。通过对预训练模型进行微调，可以将从大型数据集学习到的知识迁移至下游小型数据集，从而缓解该问题。然而，计算效率与检测性能方面的瓶颈限制了其临床应用。在模型训练过程中，难以在不显著牺牲计算效率的前提下提升检测性能。本文提出一种面向心电图心血管疾病检测的计算高效半监督学习范式（CE-SSL），旨在实现鲁棒且计算高效的心血管疾病检测。该范式能够以有限监督和高计算效率，使预训练模型在下游数据集上实现鲁棒自适应。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，提出一种单次秩分配模块，用于确定预训练权重更新矩阵的最优秩。最后，引入一种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，同时保持高计算效率。在四个下游数据集上的大量实验表明，CE-SSL不仅在多标签心血管疾病检测任务中优于现有先进方法，同时消耗更少的GPU内存占用、训练时间及参数存储空间。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。代码及补充材料详见 https://github.com/KAZABANA/CE-SSL