Dubline: a deep unfolding network for B-line detection in lung ultrasound images

Yang, Tianqi, Anantrasirichai, Nantheera, Karakus, Oktay ORCID: https://orcid.org/0000-0001-8009-9319, Allinovi, Marco, Koydemir, Hatice Ceylan and Achim, Alin 2024. Dubline: a deep unfolding network for B-line detection in lung ultrasound images. Presented at: IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, pp. 1-4. 10.1109/ISBI56570.2024.10635643

Preview

PDF - Accepted Post-Print Version Download (11MB) | Preview

Abstract

In the context of lung ultrasound, the identification of B-lines, which serve as indicators of interstitial lung disease and pulmonary edema, holds immense significance in clinical diagnosis. Presently, although vision-based automatic B-line detection techniques have emerged, their performance remains suboptimal. This paper introduces a novel approach, framing B-line detection as an inverse problem through the deep unfolding of the Alternating Direction Method of Multipliers. By leveraging the capabilities of deep neural networks and model-based methods, this methodology addresses the challenges associated with data labeling and model training in lung ultrasound image analysis. Our primary aim is to significantly augment diagnostic precision while maintaining efficient real-time capabilities. The experiment on 34 patients demonstrates that the proposed method outperforms traditional model-based approaches, achieving a 10.6% higher F 1 score and running over 90 times faster, underscoring its potential for real-time clinical utility.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	979-8-3503-1333-8
Funders:	N/A
Date of First Compliant Deposit:	11 October 2024
Date of Acceptance:	22 August 2024
Last Modified:	14 Oct 2024 14:00
URI:	https://orca.cardiff.ac.uk/id/eprint/172860

Actions (repository staff only)

Edit Item