  Studies have proved that the number of B-lines in lung ultrasound images has
a strong statistical link to the amount of extravascular lung water, which is
significant for hemodialysis treatment. Manual inspection of B-lines requires
experts and is time-consuming, whilst modelling automation methods is currently
problematic because of a lack of ground truth. Therefore, in this paper, we
propose a novel semi-supervised learning method for the B-line detection task
based on contrastive learning. Through multi-level unsupervised learning on
unlabelled lung ultrasound images, the features of the artefacts are learnt. In
the downstream task, we introduce a fine-tuning process on a small number of
labelled images using the EIoU-based loss function. Apart from reducing the
data labelling workload, the proposed method shows a superior performance to
model-based algorithm with the recall of 91.43%, the accuracy of 84.21% and the
F1 score of 91.43%.
