  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.
