A Semi-Supervised Learning Approach for B-Line Detection in Lung Ultrasound Images

Tianqi Yang*, Nantheera Anantrasirichai, Oktay Karakus, Marco Allinovi, Alin Achim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

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 designing automatic methods is currently problematic because of the 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 arte-facts 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 compared to model-based methods with the recall of 91.43%, the accuracy of 84.21% and the F1 score of 91.43%.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
Publication statusPublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: 18 Apr 202321 Apr 2023
https://biomedicalimaging.org/2023/

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period18/04/2321/04/23
Internet address

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • B-line detection
  • Contrastive learning
  • EIoU loss
  • lung ultrasound
  • unsupervised learning

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