Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
This provides a robust solution for real-time diagnostic support in COVID-19 detection using ultrasound, addressing a domain-specific medical imaging challenge.
The paper tackled the problem of limited and poorly annotated lung ultrasound datasets for COVID-19 severity scoring by proposing MeDiVLAD, a pipeline that outperformed conventional fully-supervised methods in frame- and video-level scoring with minimal fine-tuning.
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.