  The identification of artefacts, particularly B-lines, in lung ultrasound
(LUS), is crucial for assisting clinical diagnosis, prompting the development
of innovative methodologies. While the Cauchy proximal splitting (CPS)
algorithm has demonstrated effective performance in B-line detection, the
process is slow and has limited generalization. This paper addresses these
issues with a novel unsupervised deep unfolding network structure (DUCPS). The
framework utilizes deep unfolding procedures to merge traditional model-based
techniques with deep learning approaches. By unfolding the CPS algorithm into a
deep network, DUCPS enables the parameters in the optimization algorithm to be
learnable, thus enhancing generalization performance and facilitating rapid
convergence. We conducted entirely unsupervised training using the
Neighbor2Neighbor (N2N) and the Structural Similarity Index Measure (SSIM)
losses. When combined with an improved line identification method proposed in
this paper, state-of-the-art performance is achieved, with the recall and F2
score reaching 0.70 and 0.64, respectively. Notably, DUCPS significantly
improves computational efficiency eliminating the need for extensive data
labeling, representing a notable advancement over both traditional algorithms
and existing deep learning approaches.
