Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound

Blake VanBerlo, Brian Li, Alexander Wong, Jesse Hoey, Robert Arntfield

CVPRW 2023 pp. 3077-3086

doi:10.1109/CVPRW59228.2023.00309 /cvprw/2023/vanberlo2023cvprw-exploring/

Abstract

Self-supervised pretraining has been observed to improve performance in supervised learning tasks in medical imaging. This study investigates the utility of self-supervised pretraining prior to conducting supervised fine-tuning for the downstream task of lung sliding classification in M-mode lung ultrasound images. We propose a novel pairwise relationship that couples M-mode images constructed from the same B-mode image and investigate the utility of data augmentation procedure specific to M-mode lung ultrasound. The results indicate that self-supervised pretraining yields better performance than full supervision, most notably for feature extractors not initialized with ImageNet-pretrained weights. Moreover, we observe that including a vast volume of unlabelled data results in improved performance on external validation datasets, underscoring the value of self-supervision for improving generalizability in automatic ultrasound interpretation. To the authors’ best knowledge, this study is the first to characterize the influence of self-supervised pretraining for M-mode ultrasound.

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Cite

Text

VanBerlo et al. "Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00309

Markdown

[VanBerlo et al. "Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/vanberlo2023cvprw-exploring/) doi:10.1109/CVPRW59228.2023.00309

BibTeX

@inproceedings{vanberlo2023cvprw-exploring,
  title     = {{Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound}},
  author    = {VanBerlo, Blake and Li, Brian and Wong, Alexander and Hoey, Jesse and Arntfield, Robert},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {3077-3086},
  doi       = {10.1109/CVPRW59228.2023.00309},
  url       = {https://mlanthology.org/cvprw/2023/vanberlo2023cvprw-exploring/}
}