Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
Journal:
arXiv
Published Date:
Jan 21, 2025
Abstract
With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a
promising technique for COVID-19 detection, due to its non-invasive nature,
affordability, and portability. In response, researchers have focused on
developing AI-based scoring systems to provide real-time diagnostic support.
However, the limited size and lack of proper annotation in publicly available
ultrasound datasets pose significant challenges for training a robust AI model.
This paper proposes MeDiVLAD, a novel pipeline to address the above issue for
multi-level lung-ultrasound (LUS) severity scoring. In particular, we leverage
self-knowledge distillation to pretrain a vision transformer (ViT) without
label and aggregate frame-level features via dual-level VLAD aggregation. We
show that with minimal finetuning, MeDiVLAD outperforms conventional
fully-supervised methods in both frame- and video-level scoring, while offering
classification reasoning with exceptional quality. This superior performance
enables key applications such as the automatic identification of critical lung
pathology areas and provides a robust solution for broader medical video
classification tasks.
