2022/11/25 by Tianqi Yang, Yang, Tianqi, Nantheera Anantrasirichai +7
Engineering · Medicine · #FOS: Electrical engineering #Flow Measurement and Analysis #Image and Video Processing (eess.IV) #Lung Cancer Diagnosis and Treatment #Ultrasound in Clinical Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.14050
openalex publication_date 2022/11/25 · openalex created_date 2022/11/30 · openalex updated_date 2026/07/28
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%.